One-class collaborative filtering based on matrix factorization
基于矩阵分解的单类协同过滤推荐算法

News item recommendation and bookmarks recommendation are most naturally thought of as OOCF problems. Usually this kind of data are extremely sparse, just a small fraction are positive examples. Negative examples and unlabeled positive examples are mixed together and are typically unable to distinguish them, therefore ambiguity arises in the interpretation of the non-positive example. This paper proposed a CF algorithm-weighted alternating least squares(wALS). That was, by using weighting scheme assigning "1" to observed examples and low positive real number weights to unobserved examples to reflect the confidence of positive examples and negative examples. The experimental evaluation using two real-world datasets shows that wALS achieves better results in comparison with several classical one-class collaborative filtering recommendation algorithms.