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...

Ausführliche Beschreibung

Bibliographische Detailangaben

Veröffentlicht in:	IEEE transactions on emerging topics in computational intelligence Jg. 7; H. 4; S. 1228 - 1241
Hauptverfasser:	Zhou, Wei, Liu, Yong, Li, Min, Wang, Yu, Shen, Zhiqi, Feng, Liang, Zhu, Zexuan
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Piscataway IEEE 01.08.2023 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	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-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!