Dynamic Multi-Objective Optimization Framework With Interactive Evolution for Sequential Recommendation

In contrast to traditional recommender systems which usually pay attention to users' general and long-term preferences, sequential recommendation (SR) can model users' dynamic intents based on their behaviour sequences and suggest the next item(s) to them. However, most of existing sequent...

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Bibliographic Details

Published in:	IEEE transactions on emerging topics in computational intelligence Vol. 7; no. 4; pp. 1228 - 1241
Main Authors:	Zhou, Wei, Liu, Yong, Li, Min, Wang, Yu, Shen, Zhiqi, Feng, Liang, Zhu, Zexuan
Format:	Journal Article
Language:	English
Published:	Piscataway IEEE 01.08.2023 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Behavioral sciences Dynamic multi-objective optimization Evolutionary algorithms Machine learning multi-objective recommendation Multiple objective analysis Optimization Pareto optimization Recommender systems sequential recommendation Supervised learning Tail Task analysis Training Tuning
ISSN:	2471-285X, 2471-285X
Online Access:	Get full text
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