Many applications of collaborative filtering (CF), such as news item recommendation and bookmark recommendation, are most naturally thought of as oneclass 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 oneclass problem under the CF setting. 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.
Pan et al. REF solve the one-class recommendation problem in two ways: negative example weighting and negative example sampling.
One-Class Collaborative Filtering
2008 Eighth IEEE International Conference on Data Mining
2008 Eighth IEEE International Conference on Data Mining
Computer Science