  In modern recommender systems, both users and items are associated with rich
side information, which can help understand users and items. Such information
is typically heterogeneous and can be roughly categorized into flat and
hierarchical side information. While side information has been proved to be
valuable, the majority of existing systems have exploited either only flat side
information or only hierarchical side information due to the challenges brought
by the heterogeneity. In this paper, we investigate the problem of exploiting
heterogeneous side information for recommendations. Specifically, we propose a
novel framework jointly captures flat and hierarchical side information with
mathematical coherence. We demonstrate the effectiveness of the proposed
framework via extensive experiments on various real-world datasets. Empirical
results show that our approach is able to lead a significant performance gain
over the state-of-the-art methods.
