Incremental one-class collaborative filtering with co-evolving side networks
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Incremental one-class collaborative filtering with co-evolving side networks Chen Chen1 · Yinglong Xia2 · Hui Zang1 · Jundong Li3 · Huan Liu4 · Hanghang Tong5 Received: 12 March 2019 / Revised: 26 August 2020 / Accepted: 30 August 2020 © Springer-Verlag London Ltd., part of Springer Nature 2020
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 oneclass 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. Keywords Incremental algorithms · One-class collaborative filtering · Evolving networks
1 Introduction The past decade has witnessed the prosperity of recommender systems in various applications, ranging from e-commerce platforms to online service providers. Among the numerous recommendation algorithms in the literature, collaborative filtering-based methods are widely adopted in many applications due to its superior effectiveness. Traditional collaborative filtering algorithms are typically designed to provide recommendations based on users’ explicit, multi-scale feedback (e.g., rating 1–5). However, in many real applications, the preferences
The work was done while the first three authors were working at Futurewei Technologies, Inc. Extended author information available on the last page of the article
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Fig. 1 An illustration of online one-class recommendation problem with side networks. Solid lines represent the links in the original system, dashed lines represent the newly emerged links (best viewed in color)
might only be inferred from users’ implicit, one-class feedback (e.g., actions or inactions). For example, it is reasonable to infer that a user likes a song if s/he listened to it from the beginning to the end; otherwise, s/he may not be into the so
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