NDLI: Aggregating preference graphs for collaborative rating prediction

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Aggregating preference graphs for collaborative rating prediction

Content Provider	ACM Digital Library
Author	Mitra, Pabitra Desarkar, Maunendra Sankar Sarkar, Sudeshna
Abstract	Collaborative filtering is a widely used technique for rating prediction in recommender systems. Memory based collaborative filtering algorithms assign weights to the users to capture similarities between them. The weighted average of similar users' ratings for the test item is output as prediction. We propose a memory based algorithm that is markedly different from the existing approaches. We use preference relations instead of absolute ratings for similarity calculations, as preference relations between items are generally more consistent than ratings across like-minded users. Each user's ratings are viewed as a preference graph. Similarity weights are learned using an iterative method motivated by online learning. These weights are used to create an aggregate preference graph. Ratings are inferred to maximally agree with this aggregate graph. The use of preference relations allows the rating of an item to be influenced by other items, which is not the case in the weighted-average approaches of the existing techniques. This is very effective when the data is sparse, specially for the items rated by few users. Our experiments show that the our method outperforms other methods in the sparse regions. However, for dense regions, sometimes our results are comparable to the competing approaches, and sometimes worse.
Starting Page	21
Ending Page	28
Page Count	8
File Format	PDF QT / MOV
ISBN	9781605589060
DOI	10.1145/1864708.1864716
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2010-09-26
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Collaborative filtering Information retrieval Online learning Recommender systems
Content Type	Video Text
Resource Type	Article

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