Improving One-Class Collaborative Filtering by incorporating rich user information

One-Class Collaborative Filtering (OCCF) is an emerging setup in collaborative filtering in which only positive examples or implicit feedback can be observed. Compared with the traditional collaborative filtering setting where the data has ratings, OCCF is more realistic in many scenarios when no ratings are available. In this paper, we propose to improve OCCF accuracy by exploiting the rich user information that is often naturally available in community-based interactive information systems, including a user's search query history, purchasing and browsing activities. We propose two ways to incorporate such user information into the OCCF models: one is to linearly combine scores from different sources and the other is to embed user information into collaborative filtering. Experimental results on a large-scale retail data set from a major e-commerce company show that the proposed methods are effective and can improve the performance of the One-Class Collaborative Filtering over baseline methods through leveraging rich user information.