Event-based incremental recommendation via factors mixed Hawkes process

Zhihong CUI, Xiangguo SUN, Li PAN, Shijun LIU, Guandong XU

Research output: Contribution to journalArticlespeer-review

6 Citations (Scopus)

Abstract

Incremental recommendation systems have garnered significant research interest since they ideally adapt to users' ongoing events (such as clicking, browsing, and reviewing) and recommend items without retaining the model. Many methods have tracked the event generation sequences for incremental recommendation. However, most existing models treat the event as a static snapshot or a black box, ignoring the underlying factors that may trigger the event generation. Underlying such inner factors can help RS reasonably and foreseeingly evaluate the potential items for the user next time. Along this vein, we propose the Factors Mixed Hawkes Process (FMHP) for event-based incremental recommendations. First, we extend each event to a notion of factor-driven event sequence. Next, we consider three factors that may influence the occurrence of an event: intrinsic intensity, external intensity, and historical intensity. An intrinsic intensity function, multi-type temporal attention, and a hybrid time decay function are incorporated in FMHP to evaluate the intrinsic, external, and historical intensity, respectively. In addition, an incremental updating strategy is implemented in FMHP, continuously updating event intensity as new events occur. We conduct extensive experiments on four public datasets (e.g., Amazon Beauty, LastFM, Movielens, and Amazon Book). Compared with state-of-the-art incremental recommendation methods, our proposed FMHP model achieves superior performance, with up to 9.77%, 9.35%, 9.32%, 10.10% w.r.t. HR, 8.86%, 10.26%, 9.81%, 9.38% w.r.t. NDCG, and 9.64%, 9.32%, 8.97%,9.85% w.r.t. Recall in Beauty, LastFM, MovieLens, and Book, respectively. Besides, the case study shows that the three factors in our proposed FMHP method play a vital role in triggering event generation. Copyright © 2023 Elsevier Inc. All rights reserved.

Original languageEnglish
Article number119007
JournalInformation Sciences
Volume639
Early online dateApr 2023
DOIs
Publication statusPublished - Aug 2023

Citation

Cui, Z., Sun, X., Pan, L., Liu, S., & Xu, G. (2023). Event-based incremental recommendation via factors mixed Hawkes process. Information Sciences, 639, Article 119007. https://doi.org/10.1016/j.ins.2023.119007

Fingerprint

Dive into the research topics of 'Event-based incremental recommendation via factors mixed Hawkes process'. Together they form a unique fingerprint.