Paper Info
Title
Incremental One-Class Collaborative Filtering With Co-Evolving Side Networks
Abstract
One-class collaborative filtering (OCCF) is a fundamental research problem in a myriad of applications where the preferences of users can only be implicitly inferred from their one-class feedback (e.g., click an ad or purchase a product). The main challenges of OCCF lie in the sparsity of user feedback and the ambiguity of unobserved preferences. To effectively address the above two challenges, side networks from users and items are extensively exploited by state-of-the-art methods, which are predominantly focused on static settings. However, as real-world recommender systems evolve over time (where both the user-item ratings and user-user/item-item side networks will change), it is necessary to update OCCF results (e.g., the latent features of users and items) accordingly. The main obstacle for OCCF online update with co-evolving side networks lies in the fact that the coupled system is highly sensitive to local changes, which may cause massive perturbation on the latent features of a large number of users and items. In this paper, we propose a novel incremental one-class collaborative filtering (OCCF) method that can cope with co-evolving side networks efficiently. In particular, we model the evolution of latent features as a linear transformation process, which enables fast update of the latent features on the fly. Empirical experiments demonstrate that our method can provide high-quality recommendation results on real-world datasets.
Year
DOI
Venue
2021
10.1007/s10115-020-01511-x
KNOWLEDGE AND INFORMATION SYSTEMS
Keywords
DocType
Volume
Incremental algorithms, One-class collaborative filtering, Evolving networks
Journal
63
Issue
ISSN
Citations 
1
0219-1377
1
PageRank 
References 
Authors
0.37
0
6
Name
Order
Citations
PageRank
Chen Chen1233.45
Xia, Yinglong221726.91
Hui Zang3105277.25
Jundong Li470950.13
Huan Liu512695741.34
Hanghang Tong63560202.37