A Weighted One Class Collaborative Filtering with Content Topic Features.

A task that naturally emerges in recommender system is to improve user experience through personalized recommendations based on user's implicit feedback, such as news recommendation and scientific paper recommendation. Recommendations dealing with implicit feedback are most thought of as One Class Collaborative Filtering (OCCF), which only positive examples can be observed and the majority of data are missing. The idea to introduce weights for treating missing data as negatives has been shown to help in OCCF. But existing weighting approaches mainly use the statistical properties of feedback to determine the weight, which are not very reasonable and not personalized for each user-item pair. In this paper, we propose to improve recommendation by considering the rich user and item content information to assist weighting the unknown data in OCCF. To incorporate the useful content information, we get a content topic feature for each user and item by using probabilistic topic modeling method, and determine the personalized weight of every unknown user-item pair by these content topic features. Extensive experiments show that our algorithm can achieve better performance than the state-of-art methods. © Springer-Verlag 2012.