User talk:Ahsh: Difference between revisions
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== Problem Statement: == | == Problem Statement: == | ||
The underlying intelligent tools behind the webshoppers such as Amazon,Netflix,and Apple learn a suggestion function based on the the current user's and the others | The underlying intelligent tools behind the webshoppers such as Amazon, Netflix, and Apple learn a suggestion function based on the the current user's and the others ratings in order to offer personalized recommendations. To this end, collaborative filtering provided a promising approach in which the rating patterns (of the products) by the current user and the others are used to estimate rates (or ranking) for unrated items. The task is more challenging once the user is unknown for the system (i.e., there is not any rating records from this user). Two different strategies might be incorporated for offering the recommendation list: rating or ranking. Ranking is different from rating in which the set of recommendations is obtaind directly, rather than first finding the rates and then sort them accordingly. For collaborative ratings, Maximum Marging Matrix Factorization (MMMF) had a promising result for estimating the unknown rates. This paper extends the use of MMMF for collaborative ranking. | ||
=== Objectives: === | === Objectives: === | ||
The algorithm should | |||
1- | 1- directly optimize the ranking scores, | ||
2- | 2- be adaptable to different scores, | ||
3- | 3- not need any features extraction besides the actual ratings, | ||
4- | 4- be scalable and parralizable with large number of items and users. | ||
=== Definitions: === | === Definitions: === | ||
===== Normalized Discounted Comulative Gain ===== | |||
Revision as of 20:58, 27 July 2009
Welcome to Wiki Course Notes! We hope you will contribute much and well. You will probably want to read the help pages. Again, welcome and have fun! WikiSysop 20:18, 25 July 2009 (UTC)
CoFi_RANK: Maximum Margin Matrix Factorization for Collaborative Ranking
Problem Statement:
The underlying intelligent tools behind the webshoppers such as Amazon, Netflix, and Apple learn a suggestion function based on the the current user's and the others ratings in order to offer personalized recommendations. To this end, collaborative filtering provided a promising approach in which the rating patterns (of the products) by the current user and the others are used to estimate rates (or ranking) for unrated items. The task is more challenging once the user is unknown for the system (i.e., there is not any rating records from this user). Two different strategies might be incorporated for offering the recommendation list: rating or ranking. Ranking is different from rating in which the set of recommendations is obtaind directly, rather than first finding the rates and then sort them accordingly. For collaborative ratings, Maximum Marging Matrix Factorization (MMMF) had a promising result for estimating the unknown rates. This paper extends the use of MMMF for collaborative ranking.
Objectives:
The algorithm should
1- directly optimize the ranking scores, 2- be adaptable to different scores, 3- not need any features extraction besides the actual ratings, 4- be scalable and parralizable with large number of items and users.