2008 Eighth IEEE International Conference on Data Mining (2008)
Dec. 15, 2008 to Dec. 19, 2008
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ICDM.2008.16
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. In this paper, we consider the one-class problem under the CF setting. We propose two frameworks to tackle OCCF.
Collaborative Filtering, One-Class, Low-Rank Approximations, Alternating Least Squares
M. Scholz et al., "One-Class Collaborative Filtering," 2008 Eighth IEEE International Conference on Data Mining(ICDM), vol. 00, no. , pp. 502-511, 2008.