Event-based incremental recommendation via factors mixed Hawkes process

Cui, Z; Sun, X; Pan, L; Liu, S; Xu, G

Event-based incremental recommendation via factors mixed Hawkes process

Cui, Z Sun, X Pan, L Liu, S Xu, G

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Publisher:: Elsevier
Publication Type:: Journal Article
Citation:: Information Sciences, 2023, 639, pp. 119007
Issue Date:: 2023-08-01

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Field	Value	Language
dc.contributor.author	Cui, Z
dc.contributor.author	Sun, X
dc.contributor.author	Pan, L
dc.contributor.author	Liu, S
dc.contributor.author	Xu, G
dc.date.accessioned	2024-04-26T02:32:47Z
dc.date.available	2024-04-26T02:32:47Z
dc.date.issued	2023-08-01
dc.identifier.citation	Information Sciences, 2023, 639, pp. 119007
dc.identifier.issn	0020-0255
dc.identifier.uri	http://hdl.handle.net/10453/178388
dc.description.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.
dc.language	en
dc.publisher	Elsevier
dc.relation.ispartof	Information Sciences
dc.relation.isbasedon	10.1016/j.ins.2023.119007
dc.rights	info:eu-repo/semantics/embargoedAccess
dc.subject	01 Mathematical Sciences, 08 Information and Computing Sciences, 09 Engineering
dc.subject.classification	Artificial Intelligence & Image Processing
dc.subject.classification	40 Engineering
dc.subject.classification	46 Information and computing sciences
dc.subject.classification	49 Mathematical sciences
dc.title	Event-based incremental recommendation via factors mixed Hawkes process
dc.type	Journal Article
utslib.citation.volume	639
utslib.for	01 Mathematical Sciences
utslib.for	08 Information and Computing Sciences
utslib.for	09 Engineering
utslib.copyright.status	embargoed	*
utslib.copyright.embargo	2025-08-01T00:00:00+1000Z
dc.date.updated	2024-04-26T02:32:44Z
pubs.publication-status	Published
pubs.volume	639

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.

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http://hdl.handle.net/10453/178388