Learning Multiple Similarities of Users and Items in Recommender Systems
Huiyuan Chen, Jing Li · 2017
In addition to the sparse user-item (U-I) matrix, an increasing number of current recommender systems seek to improve performance by exploiting extra heterogeneous data sources (e.g., online social networks). Such rich side sources can provide very useful information about users' personal behaviors and items' properties, therefore can significantly benefit recommender systems. Most existing work can only incorporate a single side source of users or items. In many real-life applications, however, there exist multiple side sources for both users and items, which may be constructed from multiple domains. To incorporate multiple sources, we propose a flexible and robust algorithm called MSUI (Learning Multiple Similarities of Users and Items). The key idea of MSUI is to simultaneously capture the associations between latent factors in the U-I matrix and multiple side sources of users and items through joint nonnegative matrix factorization. MSUI has several advantages over existing methods. First, it seamlessly integrates information from the U-I matrix and information from multiple side sources. Second, by incorporating multiple sources that are usually in complementary, it's more robust to noise contained in each source. Finally, it provides a unified framework that can be easily extended to incorporate additional data sources.