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View all- Fu ZNiu X(2023)Modeling Users’ Curiosity in Recommender SystemsACM Transactions on Knowledge Discovery from Data10.1145/361759818:1(1-23)Online publication date: 16-Oct-2023
Sequential and Graphical Cross-Domain Recommendations with a Multi-View Hierarchical Transfer Gate
Authors: 
Huiyuan Li, Li Yu, Xi Niu, Youfang Leng, Qihan DuAuthors Info & Claims
Huiyuan Li, Li Yu, Xi Niu, Youfang Leng, Qihan DuAuthors Info & ClaimsArticle No.: 8, Pages 1 - 28
Abstract
Cross-domain recommender systems could potentially improve the recommendation performance by means of transferring abundant knowledge from the auxiliary domain to the target domain. They could help address some key challenges in recommender systems, such as data sparsity and cold start. However, most existing cross-domain recommendation approaches represent the user preferences based on a single kind of user’s feature or behavior and fail to explore the hidden interaction effects of different kinds of features or behaviors. In this article, we propose the Sequential and Graphical Cross-Domain Recommendations with a Multi-View Hierarchical Transfer Gate (SGCross) to transfer user representations from multiple perspectives. The SGCross model constructs a user profile by learning the personal preference from a personal view, the dynamic preference from a temporal view, as well as the collaborative preference from a collaborative view. Specifically, a Multi-view Hierarchical Gate (MHG) is designed to transfer the informative representations of user knowledge on different views from the auxiliary domain separately, aiming to enhance the user representations. Furthermore, a two-stage attentive fusion module is designed to integrate transferred information at two levels: the domain level and the view level. Extensive experiments on the Amazon dataset and the Douban dataset have demonstrated that SGCross effectively improves the accuracy of cross-domain recommendations and outperforms the state-of-the-art baseline models.
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Index Terms
- Sequential and Graphical Cross-Domain Recommendations with a Multi-View Hierarchical Transfer Gate
Recommendations
Domain ranking for cross domain collaborative filtering
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January 2024
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Publication History
Published: 10 August 2023
Online AM: 19 June 2023
Accepted: 08 June 2023
Revised: 18 January 2023
Received: 19 December 2021
Published in TKDD Volume 18, Issue 1
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