Relation-aware Blocking for Scalable Recommendation Systems

Huizhi Liang, Zehao Liu, Thanet Markchom

Abstract: Recommender systems contain rich relation information. The multiple relations in a recommender system form a heterogeneous information network. How to efficiently find similar users and items based on hop-n relations in heterogeneous information networks is one significant challenge to develop scalable recommender systems in the era of big data. Hashing has been popularly used for dimensionality reduction and data size reduction. Current hashing techniques mainly focus on hashing for directly related (i.e. hop-1) features. This paper proposes to develop relation-aware hashing techniques to bridge this gap. The proposed approaches use locality sensitive hashing (LSH) and consider hop-n relations in an information network to construct user or item blocks. They help facilitate efficient neighborhood formation and recommendation making. The experiments conducted on a large-scale real-life dataset show that the proposed approaches are effective.