Joint interaction with context operation for collaborative filtering. (April 2019)

• Record Type: Journal Article
• Authors: Bai, Peizhen; Ge, Yan; Liu, Fangling; Lu, Haiping
• Is Part Of: Pattern recognition. Volume 88(2019:Apr.)
• Page Start: 729
• Page End: 738
• Publication Date: 2019-04
• Subjects: Recommender system -- Collaborative filtering -- Matrix factorization -- Context aware -- Joint interaction -- Tensor; Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2018.12.003
• Languages: English
• ISSNs: 0031-3203

Abstract

Highlights: A new model for collaborative filtering in context-aware recommendation, integrating joint interaction model with contextual operating tensor. One layer to capture user/item interactions via a tensor and another layer to capture contextual information via another tensor. Extensive experiments on four datasets and novel studies on varying contextual influence and time split recommendation. Abstract: In recommender systems, the classical matrix factorization model for collaborative filtering only considers joint interactions between users and items. In contrast, context-aware recommender systems (CARS) use contexts to improve recommendation performance. Some early CARS models treat user, item and context equally, unable to capture contextual impact accurately. More recent models perform context operations on users and items separately, leading to "double-counting" of contextual information. This paper proposes a new model, Joint Interaction with Context Operation (JICO), to integrate the joint interaction model with the context operation model, via two layers. The joint interaction layer models interactions between users and items via an interaction tensor. The context operation layer captures contextual information via a contextual operating tensor. We evaluate JICO on four datasets and conduct novel studies, including varying contextual influence and time split recommendation. JICO consistently outperforms competing methods, while providing many useful insights to assist further analysis.