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One-Class Collaborative Filtering

One-Class Collaborative Filtering
HP Laboratories


Keyword(s): collaborative filtering, one-class, missing values

Abstract: Many applications of collaborative filtering (CF), such as news item recommendation and bookmark recommendation, are most naturally thought of as one-class collaborative filtering (OCCF) problems. In these problems, the training data usually consist simply of binary data reflecting a user.s action or inaction, such as page visitation in the case of news item recommendation or webpage bookmarking in the bookmarking scenario. Usually this kind of data are extremely sparse (a small fraction are positive examples), therefore ambiguity arises in the interpretation of the non-positive examples. Negative examples and unlabeled positive examples are mixed together and we are typically unable to distinguish them. For example, we cannot really attribute a user not bookmarking a page to a lack of interest or lack of awareness of the page. Previous research addressing this one-class problem only considered it as a classification task. However, little work has been done on this problem in the CF setting. In this paper, we propose two frameworks to tackle OCCF. One is based on weighted low rank approximation; the other is based on negative example sampling. The experimental results show that our approaches significantly outperform the baselines.

14 Pages

Additional Publication Information: Submitted to ECML/PKDD 2008, The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, Antwerp, Belgium, September 15th to 19th, 2008

External Posting Date: July 16, 2008 [Fulltext]. Approved for External Publication
Internal Posting Date: July 16, 2008 [Fulltext]

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