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Modeling user preference dynamics with coupled tensor factorization for social media recommendation

Tahmasbi, Hamidreza; Jalali, Mehrdad; Shakeri, Hassan

Abstract:
An essential problem in real-world recommender systems is that user preferences are not static and users are likely to change their preferences over time. Recent studies have shown that the modelling and capturing the dynamics of user preferences lead to significant improvements on recommendation accuracy and, consequently, user satisfaction. In this paper, we develop a framework to capture user preference dynamics in a personalized manner based on the fact that changes in user preferences can vary individually. We also consider the plausible assumption that older user activities should have less influence on a user’s current preferences. We introduce an individual time decay factor for each user according to the rate of his preference dynamics to weigh the past user preferences and decrease their importance gradually. We exploit users’ demographics as well as the extracted similarities among users over time, aiming to enhance the prior knowledge about user preference dynamics, in addition to the past weighted user preferences in a developed coupled tensor factorization technique to provide top-K recommendations. The experimental results on the two real social media datasets—Last.fm and Movielens—indicate that our proposed model is better and more robust than other competitive methods in terms of recommendation accuracy and is more capable of coping with problems such as cold-start and data sparsity.

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Verlagsausgabe §
DOI: 10.5445/IR/1000129383
Veröffentlicht am 05.02.2021
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Funktionelle Grenzflächen (IFG)
Publikationstyp Zeitschriftenaufsatz
Publikationsjahr 2020
Sprache Englisch
Identifikator ISSN: 1868-5137, 1868-5145
KITopen-ID: 1000129383
Erschienen in Journal of ambient intelligence and humanized computing
Verlag Springer
Vorab online veröffentlicht am 23.12.2020
Schlagwörter User preference dynamics; Coupled tensor factorization (CTF); Temporal recommendation; Social recommendation
Nachgewiesen in Scopus
Web of Science
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