A Novel Group Recommendation Model With Two-Stage Deep Learning

Group recommendation has recently drawn a lot of attention to the recommender system community. Currently, several deep learning-based approaches are leveraged to learn preferences of groups for items and predict next items in which groups may be interested. Yet, their recommendation performance is...

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Bibliographic Details
Published in:IEEE transactions on systems, man, and cybernetics. Systems Vol. 52; no. 9; pp. 5853 - 5864
Main Authors: Huang, Zhenhua, Liu, Yajun, Zhan, Choujun, Lin, Chen, Cai, Weiwei, Chen, Yunwen
Format: Journal Article
Language:English
Published: New York IEEE 01.09.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Decision making
Deep learning
graph autoencoder
group recommendation
knowledge transferring
Preferences
Recommender systems
Representation learning
Semantics
Task analysis
Training
ISSN:2168-2216, 2168-2232
Online Access:Get full text
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Summary:Group recommendation has recently drawn a lot of attention to the recommender system community. Currently, several deep learning-based approaches are leveraged to learn preferences of groups for items and predict next items in which groups may be interested. Yet, their recommendation performance is still unsatisfactory due to sparse group-item interactions. To address this challenge, this study presents a novel model, called group recommendation model with two-stage deep learning (GRMTDL), which encompasses two sequential stages: 1) group representation learning (GRL) and 2) group preference learning (GPL). In GRL, we first construct an undirected tripartite graph over group-user-item interactions, and then employ it to accurately learn group semantic features through a spatial-based variational graph autoencoder network. While in GPL, we first introduce a dual PL-network that contains two structure-sharing subnetworks: 1) group PL-network employed for GPL and 2) user PL-network utilized for user preference learning. Then, we design a novel layered transfer learning (LTL) method to learn group preferences by alternately optimizing these two subnetworks. In particular, it can effectively absorb knowledge of user preferences into the process of GPL. Furthermore, extensive experiments on four real-world datasets demonstrate that the proposed GRMTDL model outperforms the state-of-the-art baselines for group recommendation.
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ISSN:2168-2216
2168-2232
DOI:10.1109/TSMC.2021.3131349