Improving the Accuracy of Sequential Recommendation Using a Time-aware Architecture

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dc.contributor.author Sun, Ruoxi
dc.contributor.author Yan, Jun
dc.contributor.author Ren, Fenghui
dc.contributor.author Cao, Cong
dc.date.accessioned 2023-12-26T18:36:43Z
dc.date.available 2023-12-26T18:36:43Z
dc.date.issued 2024-01-03
dc.identifier.isbn 978-0-9981331-7-1
dc.identifier.other 3a362716-ea50-4bcb-8ece-3af94f15f0b9
dc.identifier.uri https://hdl.handle.net/10125/106511
dc.language.iso eng
dc.relation.ispartof Proceedings of the 57th Hawaii International Conference on System Sciences
dc.rights Attribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.uri https://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject Data Science and Machine Learning to Support Business Decisions
dc.subject recommender system
dc.subject sequence model-ling
dc.subject time-aware
dc.title Improving the Accuracy of Sequential Recommendation Using a Time-aware Architecture
dc.type Conference Paper
dc.type.dcmi Text
dcterms.abstract Sequential recommendation methods play a critical role in modern recommender systems because of their ability to capture the context of users' interactions based on their behaviours performed recently. Despite their success, we argue that these sequential recommendation approaches usually consider the relative order of items in a sequence and ignore important time interval information. The time interval can reflect timely changes in user interests by capturing more accurate trends in the evolution of the interests. The ignorance of time intervals will make it difficult for them to learn high-quality user representation. To tackle that, in this paper, we propose a time interval-sensitive mechanism for the sequential recommendation, and we incorporate this mechanism in GRU, named it 2Gated-TimeGRU. We utilise time intervals between two consecutive user interactions as an additional model feature, which enables more accurate modelling of user short-term preferences and temporal dynamics. Experiments on real-world datasets show that the proposed approach outperforms the state-of-the-art baseline models in capturing sequential user preferences and recommendation accuracy.
dcterms.extent 10 pages
prism.startingpage 1120
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Data Science and Machine Learning to Support Business Decisions