Integrating Heterogeneous Information via Flexible Regularization Framework for Recommendation

TL;DR

This paper proposes a flexible matrix factorization framework called SimMF that integrates heterogeneous information, including attribute data and social relations, to improve recommendation accuracy, especially when social data is sparse.

Contribution

The paper introduces a novel dual regularization framework that effectively combines attribute information and social relations within a heterogeneous information network for recommendation.

Findings

Attribute information significantly enhances recommendation accuracy.

Attribute data can be more influential than social relations.

Different regularization models impact users and items differently.

Abstract

Recently, there is a surge of social recommendation, which leverages social relations among users to improve recommendation performance. However, in many applications, social relations are absent or very sparse. Meanwhile, the attribute information of users or items may be rich. It is a big challenge to exploit these attribute information for the improvement of recommendation performance. In this paper, we organize objects and relations in recommendation system as a heterogeneous information network, and introduce meta path based similarity measure to evaluate the similarity of users or items. Furthermore, a matrix factorization based dual regularization framework SimMF is proposed to flexibly integrate different types of information through adopting the similarity of users and items as regularization on latent factors of users and items. Extensive experiments not only validate the effectiveness of SimMF but also reveal some interesting findings. We find that attribute information of users and items can significantly improve recommendation accuracy, and their contribution seems more important than that of social relations. The experiments also reveal that different regularization models have obviously different impact on users and items.