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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Summary: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 sequential models learn the ranking score of an item only based on its relevance property, and the personalized user demands in terms of different learning objectives, such as diversity, tail novelty or recency, which have been played essential roles in multi-objective recommendation (MOR), are often neglected in SR. In this paper, we first discuss the importance of considering multiple different objectives within a learning model for recommender system. Next, to capture users' objective-level preferences by utilizing interactive information in the sequential context, we propose a novel Dynamic Multi-objective Recommendation (DMORec) framework with interactive evolution for SR. In particular, DMORec formulates a dynamic multi-objective optimization task to simultaneously optimize more than two varying objectives in an interactive recommendation process. Moreover, to resolve this optimization task in SR, we develop an evolutionary algorithm with supervised learning approach to obtain the Pareto-optimal solutions of the formulated problem. Comprehensive experiments on four real-world datasets demonstrate the effectiveness of the proposed DMORec for dynamic multi-objective recommendation in sequential recommender systems.
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ISSN:2471-285X
2471-285X
DOI:10.1109/TETCI.2023.3251352