NDLI: Personalized active learning for collaborative filtering

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Personalized active learning for collaborative filtering

Content Provider	ACM Digital Library
Author	Harpale, Abhay S. Yang, Yiming
Abstract	Collaborative Filtering (CF) requires user-rated training examples for statistical inference about the preferences of new users. Active learning strategies identify the most informative set of training examples through minimum interactions with the users. Current active learning approaches in CF make an implicit and unrealistic assumption that a user can provide rating for any queried item. This paper introduces a new approach to the problem which does not make such an assumption. We personalize active learning for the user, and query for only those items which the user can provide rating for. We propose an extended form of Bayesian active learning and use the Aspect Model for CF to illustrate and examine the idea. A comparative evaluation of the new method and a well-established baseline method on benchmark datasets shows statistically significant improvements with our method over the performance of the baseline method that is representative for existing approaches which do not take personalization into account.
Starting Page	91
Ending Page	98
Page Count	8
File Format	PDF
ISBN	9781605581644
DOI	10.1145/1390334.1390352
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2008-07-20
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Personalization Collaborative filtering Active learning Bayesian active learning Aspect model
Content Type	Text
Resource Type	Article

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