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Neural Additive and Basis Models with Feature Selection and Interactions Read chapter Read first chapter

2024 | OriginalPaper | Chapter

Graph-based Dynamic Preference Modeling for Personalized Recommendation

Authors : Jiaqi Wu, Yidan Xu, Bowen Zhang, Zekun Xu, Bohan Li

Published in: Advances in Knowledge Discovery and Data Mining

Publisher: Springer Nature Singapore

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Abstract

Sequential Recommendation (SR) can predict possible future behaviors by considering the user’s behavioral sequence. However, users’ preferences constantly change in practice and are difficult to track. The existing methods only consider neighbouring items and neglect the impact of non-adjacent items on user choices. Therefore, how to build an accurate recommendation model is a complex challenge. We propose a novel Graph Neural Network (GNN) based model, Graph-based Dynamic Preference Modeling for Personalized Recommendation (DPPR). In DPPR, the graph attention network (GAT) learns the features of long-term preference. The short-term graph computes items’ dependencies on link propagation between items and attributes. It adjusts node features under the user’s views. The module emphasizes skip features among entity nodes and incorporates time intervals of items to calculate the impact of non-adjacent items. Finally, we combine their representations to generate user preferences and aid decisions. The experimental results indicate that our model outperforms state-of-the-art methods on three public datasets.

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Metadata
Title
Graph-based Dynamic Preference Modeling for Personalized Recommendation
Authors
Jiaqi Wu
Yidan Xu
Bowen Zhang
Zekun Xu
Bohan Li
Copyright Year
2024
Publisher
Springer Nature Singapore
Book
Advances in Knowledge Discovery and Data Mining

Print ISBN: 978-981-9722-61-7

Electronic ISBN: 978-981-9722-59-4

Copyright Year: 2024

DOI
https://doi.org/10.1007/978-981-97-2259-4_27

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