http://wiki.math.uwaterloo.ca/statwiki/index.php?title=Point-of-Interest_Recommendation:_Exploiting_Self-Attentive_Autoencoders_with_Neighbor-Aware_Influence&feed=atom&action=historyPoint-of-Interest Recommendation: Exploiting Self-Attentive Autoencoders with Neighbor-Aware Influence - Revision history2024-03-28T18:18:24ZRevision history for this page on the wikiMediaWiki 1.41.0http://wiki.math.uwaterloo.ca/statwiki/index.php?title=Point-of-Interest_Recommendation:_Exploiting_Self-Attentive_Autoencoders_with_Neighbor-Aware_Influence&diff=49897&oldid=prevJ27ni: /* Critiques */2020-12-16T09:15:04Z<p><span dir="auto"><span class="autocomment">Critiques</span></span></p>
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<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>The paper uses dataset from Gowalla, Yelp and Foursquare, which are all very well-known for providing crowd-sourced opinions about local restaurants. The provided example in the neighbour-aware decoder also uses Lazeez and Mr. Panino as an example in our daily lives. It would be useful to see how the POI recommendation system performs using datasets that are less geared towards restaurants and more towards other aspects of our lives.</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>The paper uses dataset from Gowalla, Yelp and Foursquare, which are all very well-known for providing crowd-sourced opinions about local restaurants. The provided example in the neighbour-aware decoder also uses Lazeez and Mr. Panino as an example in our daily lives. It would be useful to see how the POI recommendation system performs using datasets that are less geared towards restaurants and more towards other aspects of our lives.</div></td></tr>
<tr><td colspan="2" class="diff-side-deleted"></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;"></ins></div></td></tr>
<tr><td colspan="2" class="diff-side-deleted"></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;">It would be a lot easier to understand the process if the pseudocode of the algorithm can be provided instead of just giving some equations.</ins></div></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>== References ==</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>== References ==</div></td></tr>
</table>J27nihttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=Point-of-Interest_Recommendation:_Exploiting_Self-Attentive_Autoencoders_with_Neighbor-Aware_Influence&diff=49793&oldid=prevM59jiang: /* Previous Work */2020-12-07T19:37:26Z<p><span dir="auto"><span class="autocomment">Previous Work</span></span></p>
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<td colspan="2" style="background-color: #fff; color: #202122; text-align: center;">Revision as of 15:37, 7 December 2020</td>
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<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>== Previous Work == </div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>== Previous Work == </div></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br></td></tr>
<tr><td class="diff-marker" data-marker="−"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>In the previous works, the method is just equally treating users checked in POIs. The drawback of equally treating users checked in POIs is that valuable information about the similarity between users is not utilized, thus reducing <del style="font-weight: bold; text-decoration: none;">the power of </del>such recommenders. However, the SAE adaptively differentiates user preference degrees in multiple aspects.</div></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>In the previous works, the method is just equally treating users checked in POIs. The drawback of equally treating users checked in POIs is that valuable information about the similarity between users is not utilized, thus reducing such recommenders<ins style="font-weight: bold; text-decoration: none;">' power</ins>. However, the SAE adaptively differentiates user preference degrees in multiple aspects.</div></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br></td></tr>
<tr><td class="diff-marker" data-marker="−"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>Previous methods mainly used a process called collaborative filtering which can be divided into memory-based methods and model-based methods. Collaborative filtering makes recommendations from historical user-system interactions like user’s feedback or browsing history. Content-based and hybrid recommendation systems are also commonly used. Content-based recommendation system compares users’ information like texts, videos and images. Hybrid model combines two or more recommendation systems. Memory-based methods predict a user preference based on a weighted average of similar users or POIs. Model-based methods use user-POI data to build a model for generating recommendations. Both methods typically model user preferences linearly, which may be an oversimplification.</div></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>Previous methods mainly used a process called collaborative filtering<ins style="font-weight: bold; text-decoration: none;">, </ins>which can be divided into memory-based methods and model-based methods. Collaborative filtering makes recommendations from historical user-system interactions like user’s feedback or browsing history. Content-based and hybrid recommendation systems are also commonly used. Content-based recommendation system compares users’ information like texts, videos<ins style="font-weight: bold; text-decoration: none;">, </ins>and images. Hybrid model combines two or more recommendation systems. Memory-based methods predict a user preference based on a weighted average of similar users or POIs. Model-based methods use user-POI data to build a model for generating recommendations. Both methods typically model user preferences linearly, which may be an oversimplification.</div></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>There are some other personalized POI recommendation methods that can be used. Some famous software (e.g., Netflix) uses model-based methods that are built on matrix factorization (MF). For example, ranked based Geographical Factorization Method in [1] adopted weighted regularized MF to serve people on POI. Machine learning is popular in this area. POI recommendation is an important topic in the domain of recommender systems [4]. This paper also described related work in Personalized location recommendation and attention mechanism in the recommendation. The recent studies on location recommendation methods using historical data (check-ins, comments, etc.)</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>There are some other personalized POI recommendation methods that can be used. Some famous software (e.g., Netflix) uses model-based methods that are built on matrix factorization (MF). For example, ranked based Geographical Factorization Method in [1] adopted weighted regularized MF to serve people on POI. Machine learning is popular in this area. POI recommendation is an important topic in the domain of recommender systems [4]. This paper also described related work in Personalized location recommendation and attention mechanism in the recommendation. The recent studies on location recommendation methods using historical data (check-ins, comments, etc.)</div></td></tr>
</table>M59jianghttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=Point-of-Interest_Recommendation:_Exploiting_Self-Attentive_Autoencoders_with_Neighbor-Aware_Influence&diff=49455&oldid=prevZ224jian: /* Critiques */2020-12-06T19:36:53Z<p><span dir="auto"><span class="autocomment">Critiques</span></span></p>
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<td colspan="2" style="background-color: #fff; color: #202122; text-align: center;">Revision as of 15:36, 6 December 2020</td>
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<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>It would be useful to the readers if the authors briefly described covariates with which they are using to predict POIs. All that is detailed in this paper is that geographical information is used. Taking note of the characteristics that the data set the proposed method deals with is important, especially as phones become capabable of collecting more and more kinds of data. That is to say, the information available for predicting POI's in the future may be different to the information available today, so it is important to describe the data. (The information could even decrease in the future if privacy laws are enacted).</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>It would be useful to the readers if the authors briefly described covariates with which they are using to predict POIs. All that is detailed in this paper is that geographical information is used. Taking note of the characteristics that the data set the proposed method deals with is important, especially as phones become capabable of collecting more and more kinds of data. That is to say, the information available for predicting POI's in the future may be different to the information available today, so it is important to describe the data. (The information could even decrease in the future if privacy laws are enacted).</div></td></tr>
<tr><td colspan="2" class="diff-side-deleted"></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;"></ins></div></td></tr>
<tr><td colspan="2" class="diff-side-deleted"></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;">The paper uses dataset from Gowalla, Yelp and Foursquare, which are all very well-known for providing crowd-sourced opinions about local restaurants. The provided example in the neighbour-aware decoder also uses Lazeez and Mr. Panino as an example in our daily lives. It would be useful to see how the POI recommendation system performs using datasets that are less geared towards restaurants and more towards other aspects of our lives.</ins></div></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>== References ==</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>== References ==</div></td></tr>
</table>Z224jianhttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=Point-of-Interest_Recommendation:_Exploiting_Self-Attentive_Autoencoders_with_Neighbor-Aware_Influence&diff=48706&oldid=prevM456li: /* Critiques */2020-12-01T18:33:22Z<p><span dir="auto"><span class="autocomment">Critiques</span></span></p>
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<td colspan="2" style="background-color: #fff; color: #202122; text-align: center;">Revision as of 14:33, 1 December 2020</td>
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<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>This idea would have many applications, such as suggesting new restaurants to customers in the food delivery service app. Would the improvement in accuracy outweigh the increased complexity of the model when it comes to use in industry?</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>This idea would have many applications, such as suggesting new restaurants to customers in the food delivery service app. Would the improvement in accuracy outweigh the increased complexity of the model when it comes to use in industry?</div></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br></td></tr>
<tr><td class="diff-marker" data-marker="−"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>It would be nice if the <del style="font-weight: bold; text-decoration: none;">authours </del>could describe the extensive experiments on the real-world datasets, with different baseline methods and evaluation metrics, to demonstrate the effectiveness of the proposed model. Moreover, show the comparison result in tables vs other methodologies, both in terms of accuracy and time-efficiency. In addition, the drawbacks of this new methodology are unknown to the readers. In other words, how does this compare to the already established recommendation systems found in large scale applications utilize by companies like Netflix? Why use the proposed method over something simpler such as matrix factorization or collaborative filtering? </div></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>It would be nice if the <ins style="font-weight: bold; text-decoration: none;">authors </ins>could describe the extensive experiments on the real-world datasets, with different baseline methods and evaluation metrics, to demonstrate the effectiveness of the proposed model. Moreover, show the comparison result in tables vs other methodologies, both in terms of accuracy and time-efficiency. In addition, the drawbacks of this new methodology are unknown to the readers. In other words, how does this compare to the already established recommendation systems found in large scale applications utilize by companies like Netflix? Why use the proposed method over something simpler such as matrix factorization or collaborative filtering? </div></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>It would also be nice if the authors provided some more ablation on the various components of the proposed method. Even after reading some of their experiments, we do not have a clear understanding of how important each component is to the recommendation quality.</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>It would also be nice if the authors provided some more ablation on the various components of the proposed method. Even after reading some of their experiments, we do not have a clear understanding of how important each component is to the recommendation quality.</div></td></tr>
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<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>Some additional explanations can be inserted in the Objective Function part. From the paper, we can find <math>\lambda</math> is the regularization parameter and <math>W_a</math> and <math>w_t</math> are the learned parameters in the attention layer and aggregation layer.</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>Some additional explanations can be inserted in the Objective Function part. From the paper, we can find <math>\lambda</math> is the regularization parameter and <math>W_a</math> and <math>w_t</math> are the learned parameters in the attention layer and aggregation layer.</div></td></tr>
<tr><td colspan="2" class="diff-side-deleted"></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;"></ins></div></td></tr>
<tr><td colspan="2" class="diff-side-deleted"></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;">It would be better for the researchers to test the performance of the efficiency of this model, and compare it with other models to prove that this model is indeed better than other models.</ins></div></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>It would be more attractive if there is a section to introduce the applications that based on such algorithm in daily life. For instance, which application we use nowadays is based on this algorithm and what are the advantages of it compared to other similar algorithm?</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>It would be more attractive if there is a section to introduce the applications that based on such algorithm in daily life. For instance, which application we use nowadays is based on this algorithm and what are the advantages of it compared to other similar algorithm?</div></td></tr>
</table>M456lihttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=Point-of-Interest_Recommendation:_Exploiting_Self-Attentive_Autoencoders_with_Neighbor-Aware_Influence&diff=48684&oldid=prevD5cui: /* Objective Function */2020-12-01T14:02:49Z<p><span dir="auto"><span class="autocomment">Objective Function</span></span></p>
<table style="background-color: #fff; color: #202122;" data-mw="interface">
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<td colspan="2" style="background-color: #fff; color: #202122; text-align: center;">Revision as of 10:02, 1 December 2020</td>
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<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>=== Objective Function ===</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>=== Objective Function ===</div></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br></td></tr>
<tr><td class="diff-marker" data-marker="−"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>By minimizing the objective function, the partial derivatives with respect to all the parameters can be computed by gradient descent with backpropagation. After that, the training is complete.</div></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>By minimizing the objective function, the partial derivatives with respect to all the parameters can be computed by gradient descent with backpropagation. After that, the training is complete<ins style="font-weight: bold; text-decoration: none;">. By minimizing the objective function, the partial derivatives with respect to all the parameters can be computed by gradient descent with backpropagation</ins>.</div></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>[[File:objective_function.JPG|center]]</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>[[File:objective_function.JPG|center]]</div></td></tr>
<tr><td class="diff-marker" data-marker="−"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div><del style="font-weight: bold; text-decoration: none;"></del></div></td><td colspan="2" class="diff-side-added"></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>== Comparative analysis ==</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>== Comparative analysis ==</div></td></tr>
</table>D5cuihttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=Point-of-Interest_Recommendation:_Exploiting_Self-Attentive_Autoencoders_with_Neighbor-Aware_Influence&diff=48558&oldid=prevL28chang: /* Previous Work */2020-12-01T01:32:25Z<p><span dir="auto"><span class="autocomment">Previous Work</span></span></p>
<table style="background-color: #fff; color: #202122;" data-mw="interface">
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<td colspan="2" style="background-color: #fff; color: #202122; text-align: center;">Revision as of 21:32, 30 November 2020</td>
</tr><tr><td colspan="2" class="diff-lineno" id="mw-diff-left-l9">Line 9:</td>
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<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>In the previous works, the method is just equally treating users checked in POIs. The drawback of equally treating users checked in POIs is that valuable information about the similarity between users is not utilized, thus reducing the power of such recommenders. However, the SAE adaptively differentiates user preference degrees in multiple aspects.</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>In the previous works, the method is just equally treating users checked in POIs. The drawback of equally treating users checked in POIs is that valuable information about the similarity between users is not utilized, thus reducing the power of such recommenders. However, the SAE adaptively differentiates user preference degrees in multiple aspects.</div></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br></td></tr>
<tr><td class="diff-marker" data-marker="−"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>Previous methods mainly used a process called collaborative filtering which can be divided into memory-based methods and model-based methods. Memory-based methods predict a user preference based on a weighted average of similar users or POIs. Model-based methods use user-POI data to build a model for generating recommendations. Both methods typically model user preferences linearly, which may be an oversimplification.</div></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>Previous methods mainly used a process called collaborative filtering which can be divided into memory-based methods and model-based methods<ins style="font-weight: bold; text-decoration: none;">. Collaborative filtering makes recommendations from historical user-system interactions like user’s feedback or browsing history. Content-based and hybrid recommendation systems are also commonly used. Content-based recommendation system compares users’ information like texts, videos and images. Hybrid model combines two or more recommendation systems</ins>. Memory-based methods predict a user preference based on a weighted average of similar users or POIs. Model-based methods use user-POI data to build a model for generating recommendations. Both methods typically model user preferences linearly, which may be an oversimplification.</div></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>There are some other personalized POI recommendation methods that can be used. Some famous software (e.g., Netflix) uses model-based methods that are built on matrix factorization (MF). For example, ranked based Geographical Factorization Method in [1] adopted weighted regularized MF to serve people on POI. Machine learning is popular in this area. POI recommendation is an important topic in the domain of recommender systems [4]. This paper also described related work in Personalized location recommendation and attention mechanism in the recommendation. The recent studies on location recommendation methods using historical data (check-ins, comments, etc.)</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>There are some other personalized POI recommendation methods that can be used. Some famous software (e.g., Netflix) uses model-based methods that are built on matrix factorization (MF). For example, ranked based Geographical Factorization Method in [1] adopted weighted regularized MF to serve people on POI. Machine learning is popular in this area. POI recommendation is an important topic in the domain of recommender systems [4]. This paper also described related work in Personalized location recommendation and attention mechanism in the recommendation. The recent studies on location recommendation methods using historical data (check-ins, comments, etc.)</div></td></tr>
</table>L28changhttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=Point-of-Interest_Recommendation:_Exploiting_Self-Attentive_Autoencoders_with_Neighbor-Aware_Influence&diff=48351&oldid=prevX927wang: /* Previous Work */2020-11-30T06:03:59Z<p><span dir="auto"><span class="autocomment">Previous Work</span></span></p>
<table style="background-color: #fff; color: #202122;" data-mw="interface">
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<td colspan="2" style="background-color: #fff; color: #202122; text-align: center;">Revision as of 02:03, 30 November 2020</td>
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<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>== Previous Work == </div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>== Previous Work == </div></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br></td></tr>
<tr><td class="diff-marker" data-marker="−"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>In the previous works, the method is just equally treating users checked in POIs. The drawback of equally treating users checked in POIs is that valuable information about the similarity between users is not utilized, thus reducing the power of such recommenders. However, the SAE adaptively differentiates <del style="font-weight: bold; text-decoration: none;">the </del>user preference degrees in multiple aspects.</div></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>In the previous works, the method is just equally treating users checked in POIs. The drawback of equally treating users checked in POIs is that valuable information about the similarity between users is not utilized, thus reducing the power of such recommenders. However, the SAE adaptively differentiates user preference degrees in multiple aspects.</div></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br></td></tr>
<tr><td class="diff-marker" data-marker="−"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>Previous methods mainly used a process called collaborative filtering which can be divided into memory based methods and model based methods. Memory based methods predict a <del style="font-weight: bold; text-decoration: none;">users </del>preference based on a weighted average of similar users or POIs. Model based methods use user-POI data to build a model for generating recommendations. Both methods typically model user preferences linearly, which may be an oversimplification.</div></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>Previous methods mainly used a process called collaborative filtering which can be divided into memory<ins style="font-weight: bold; text-decoration: none;">-</ins>based methods and model<ins style="font-weight: bold; text-decoration: none;">-</ins>based methods. Memory<ins style="font-weight: bold; text-decoration: none;">-</ins>based methods predict a <ins style="font-weight: bold; text-decoration: none;">user </ins>preference based on a weighted average of similar users or POIs. Model<ins style="font-weight: bold; text-decoration: none;">-</ins>based methods use user-POI data to build a model for generating recommendations. Both methods typically model user preferences linearly, which may be an oversimplification.</div></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>There are some other personalized POI recommendation methods that can be used. Some famous software (e.g., Netflix) uses model-based methods that are built on matrix factorization (MF). For example, ranked based Geographical Factorization Method in [1] adopted weighted regularized MF to serve people on POI. Machine learning is popular in this area. POI recommendation is an important topic in the domain of recommender systems [4]. This paper also described related work in Personalized location recommendation and attention mechanism in the recommendation. The recent studies on location recommendation methods using historical data (check-ins, comments, etc.)</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>There are some other personalized POI recommendation methods that can be used. Some famous software (e.g., Netflix) uses model-based methods that are built on matrix factorization (MF). For example, ranked based Geographical Factorization Method in [1] adopted weighted regularized MF to serve people on POI. Machine learning is popular in this area. POI recommendation is an important topic in the domain of recommender systems [4]. This paper also described related work in Personalized location recommendation and attention mechanism in the recommendation. The recent studies on location recommendation methods using historical data (check-ins, comments, etc.)</div></td></tr>
</table>X927wanghttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=Point-of-Interest_Recommendation:_Exploiting_Self-Attentive_Autoencoders_with_Neighbor-Aware_Influence&diff=48304&oldid=prevJ244yang: /* Previous Work */2020-11-30T04:51:28Z<p><span dir="auto"><span class="autocomment">Previous Work</span></span></p>
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<td colspan="2" style="background-color: #fff; color: #202122; text-align: center;">Revision as of 00:51, 30 November 2020</td>
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<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>Previous methods mainly used a process called collaborative filtering which can be divided into memory based methods and model based methods. Memory based methods predict a users preference based on a weighted average of similar users or POIs. Model based methods use user-POI data to build a model for generating recommendations. Both methods typically model user preferences linearly, which may be an oversimplification.</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>Previous methods mainly used a process called collaborative filtering which can be divided into memory based methods and model based methods. Memory based methods predict a users preference based on a weighted average of similar users or POIs. Model based methods use user-POI data to build a model for generating recommendations. Both methods typically model user preferences linearly, which may be an oversimplification.</div></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br></td></tr>
<tr><td class="diff-marker" data-marker="−"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>There are some other personalized POI recommendation methods that can be used. Some famous software (e.g., Netflix) uses model-based methods that are built on matrix factorization (MF). For example, ranked based Geographical Factorization Method in [1] adopted weighted regularized MF to serve people on POI. Machine learning is popular in this area. POI recommendation is an important topic in the domain of recommender systems [4]. This paper also described related work in Personalized location recommendation and attention mechanism in the recommendation.</div></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>There are some other personalized POI recommendation methods that can be used. Some famous software (e.g., Netflix) uses model-based methods that are built on matrix factorization (MF). For example, ranked based Geographical Factorization Method in [1] adopted weighted regularized MF to serve people on POI. Machine learning is popular in this area. POI recommendation is an important topic in the domain of recommender systems [4]. This paper also described related work in Personalized location recommendation and attention mechanism in the recommendation. <ins style="font-weight: bold; text-decoration: none;">The recent studies on location recommendation methods using historical data (check-ins, comments, etc.)</ins></div></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>== Motivation == </div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>== Motivation == </div></td></tr>
</table>J244yanghttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=Point-of-Interest_Recommendation:_Exploiting_Self-Attentive_Autoencoders_with_Neighbor-Aware_Influence&diff=48290&oldid=prevIaoellme: /* Introduction */2020-11-30T04:33:48Z<p><span dir="auto"><span class="autocomment">Introduction</span></span></p>
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<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>== Introduction == </div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>== Introduction == </div></td></tr>
<tr><td class="diff-marker" data-marker="−"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>With the development of mobile devices and location-acquisition technologies, accessing real-time location information is becoming easier and more efficient. Precisely because of this development, Location-based Social Networks (LBSNs) like Yelp and Foursquare have become an important part of <del style="font-weight: bold; text-decoration: none;">human’s </del>life. People can share their experiences in locations, such as restaurants and parks, on the Internet. These locations can be seen as a Point-of-Interest (POI) in software such as Maps on our phone. These large amounts of user-POI interaction data can provide a service, which is called personalized POI recommendation, to give recommendations to users <del style="font-weight: bold; text-decoration: none;">that the </del>location they might be interested in. These large amounts of data can be used to train a model through Machine Learning methods(i.e., Classification, Clustering, etc.) to predict a POI that users might be interested in. The POI recommendation system still faces some challenging issues: (1) the difficulty of modeling complex user-POI interactions from sparse implicit feedback; (2) the difficulty of incorporating geographic background information. In order to meet these challenges, this paper will introduce a novel autoencoder-based model to learn non-linear user-POI relations, which is called SAE-NAD. SAE stands for the self-attentive encoder, while NAD stands for the neighbor-aware decoder. <del style="font-weight: bold; text-decoration: none;">Autoencoder </del>is an unsupervised learning technique that we implement in the neural network model for representation learning, meaning that our neural network will contain a "bottleneck" layer that produces a compressed knowledge representation of the original input. This method will <del style="font-weight: bold; text-decoration: none;">include </del>machine learning knowledge that we learned in this course.</div></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>With the development of mobile devices and location-acquisition technologies, accessing real-time location information is becoming easier and more efficient. Precisely because of this development, Location-based Social Networks (LBSNs) like Yelp and Foursquare have become an important part of <ins style="font-weight: bold; text-decoration: none;">human </ins>life. People can share their experiences in locations, such as restaurants and parks, on the Internet. These locations can be seen as a Point-of-Interest (POI) in software such as Maps on our phone. These large amounts of user-POI interaction data can <ins style="font-weight: bold; text-decoration: none;">be used to </ins>provide a service, which is called personalized POI recommendation, to give recommendations to users <ins style="font-weight: bold; text-decoration: none;">of a </ins>location they might be interested in. These large amounts of data can be used to train a model through Machine Learning methods(i.e., Classification, Clustering, etc.) to predict a POI that users might be interested in. The POI recommendation system still faces some challenging issues: (1) the difficulty of modeling complex user-POI interactions from sparse implicit feedback; (2) the difficulty of incorporating geographic background information. In order to meet these challenges, this paper will introduce a novel autoencoder-based model to learn non-linear user-POI relations, which is called SAE-NAD. SAE stands for the self-attentive encoder, while NAD stands for the neighbor-aware decoder. <ins style="font-weight: bold; text-decoration: none;">An autoencoder </ins>is an unsupervised learning technique that we implement in the neural network model for representation learning, meaning that our neural network will contain a "bottleneck" layer that produces a compressed knowledge representation of the original input. This method will <ins style="font-weight: bold; text-decoration: none;">utilize </ins>machine learning knowledge that we learned in this course.</div></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>== Previous Work == </div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>== Previous Work == </div></td></tr>
</table>Iaoellmehttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=Point-of-Interest_Recommendation:_Exploiting_Self-Attentive_Autoencoders_with_Neighbor-Aware_Influence&diff=48286&oldid=prevIaoellme: /* Previous Work */2020-11-30T04:30:26Z<p><span dir="auto"><span class="autocomment">Previous Work</span></span></p>
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<td colspan="2" style="background-color: #fff; color: #202122; text-align: center;">Revision as of 00:30, 30 November 2020</td>
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<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>In the previous works, the method is just equally treating users checked in POIs. The drawback of equally treating users checked in POIs is that valuable information about the similarity between users is not utilized, thus reducing the power of such recommenders. However, the SAE adaptively differentiates the user preference degrees in multiple aspects.</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>In the previous works, the method is just equally treating users checked in POIs. The drawback of equally treating users checked in POIs is that valuable information about the similarity between users is not utilized, thus reducing the power of such recommenders. However, the SAE adaptively differentiates the user preference degrees in multiple aspects.</div></td></tr>
<tr><td colspan="2" class="diff-side-deleted"></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;"></ins></div></td></tr>
<tr><td colspan="2" class="diff-side-deleted"></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;">Previous methods mainly used a process called collaborative filtering which can be divided into memory based methods and model based methods. Memory based methods predict a users preference based on a weighted average of similar users or POIs. Model based methods use user-POI data to build a model for generating recommendations. Both methods typically model user preferences linearly, which may be an oversimplification.</ins></div></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>There are some other personalized POI recommendation methods that can be used. Some famous software (e.g., Netflix) uses model-based methods that are built on matrix factorization (MF). For example, ranked based Geographical Factorization Method in [1] adopted weighted regularized MF to serve people on POI. Machine learning is popular in this area. POI recommendation is an important topic in the domain of recommender systems [4]. This paper also described related work in Personalized location recommendation and attention mechanism in the recommendation.</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>There are some other personalized POI recommendation methods that can be used. Some famous software (e.g., Netflix) uses model-based methods that are built on matrix factorization (MF). For example, ranked based Geographical Factorization Method in [1] adopted weighted regularized MF to serve people on POI. Machine learning is popular in this area. POI recommendation is an important topic in the domain of recommender systems [4]. This paper also described related work in Personalized location recommendation and attention mechanism in the recommendation.</div></td></tr>
</table>Iaoellme