Exploiting high-order behaviour patterns for cross-domain sequential recommendation
The cross-domain sequential recommendation aims to predict the next item based on a sequence of recorded user behaviours in multiple domains. We propose a novel Cross-domain Sequential Recommendation approach with Graph-Collaborative Filtering (CsrGCF) to alleviate the sparsity issue of user-interac...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2022-12-01
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| Series: | Connection Science |
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| Online Access: | http://dx.doi.org/10.1080/09540091.2022.2136141 |
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| _version_ | 1850235530336272384 |
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| author | Bingyuan Wang Baisong Liu Hao Ren Xueyuan Zhang Jiangcheng Qin Qian Dong Jiangbo Qian |
| author_facet | Bingyuan Wang Baisong Liu Hao Ren Xueyuan Zhang Jiangcheng Qin Qian Dong Jiangbo Qian |
| author_sort | Bingyuan Wang |
| collection | DOAJ |
| description | The cross-domain sequential recommendation aims to predict the next item based on a sequence of recorded user behaviours in multiple domains. We propose a novel Cross-domain Sequential Recommendation approach with Graph-Collaborative Filtering (CsrGCF) to alleviate the sparsity issue of user-interaction data. Specifically, we design time-aware and relation-aware graph attention mechanisms with collaborative filtering to exploit high-order behaviour patterns of users for promising results in both domains. Time-aware Graph Attention mechanism (TGAT) is designed to learn the inter-domain sequence-level representation of items. Relationship-aware Graph Attention mechanism (RGAT) is proposed to learn collaborative items' and users' feature representations. Moreover, to simultaneously improve the recommendation performance in the two domains, a Cross-domain Feature Bidirectional Transfer module (CFBT) is proposed, transferring user's common sharing features in both domains and retaining user's domain-specific features in a specific domain. Finally, cross-domain and sequential information jointly recommend the next items that users like. We conduct extensive experiments on two real-world datasets that show that CsrGCF outperforms several state-of-the-art baselines in terms of Recall and MRR. These demonstrate the necessity of exploiting high-order behaviour patterns of users for a cross-domain sequential recommendation. Meanwhile, retaining domain-specific features is an important step in the process of cross-domain feature bidirectional transferring. |
| format | Article |
| id | doaj-art-18651d7b51f14f06b910ffb45b702238 |
| institution | OA Journals |
| issn | 0954-0091 1360-0494 |
| language | English |
| publishDate | 2022-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Connection Science |
| spelling | doaj-art-18651d7b51f14f06b910ffb45b7022382025-08-20T02:02:13ZengTaylor & Francis GroupConnection Science0954-00911360-04942022-12-013412597261410.1080/09540091.2022.21361412136141Exploiting high-order behaviour patterns for cross-domain sequential recommendationBingyuan Wang0Baisong Liu1Hao Ren2Xueyuan Zhang3Jiangcheng Qin4Qian Dong5Jiangbo Qian6Ningbo UniversityNingbo UniversityNingbo UniversityNingbo UniversityNingbo UniversityNingbo UniversityNingbo UniversityThe cross-domain sequential recommendation aims to predict the next item based on a sequence of recorded user behaviours in multiple domains. We propose a novel Cross-domain Sequential Recommendation approach with Graph-Collaborative Filtering (CsrGCF) to alleviate the sparsity issue of user-interaction data. Specifically, we design time-aware and relation-aware graph attention mechanisms with collaborative filtering to exploit high-order behaviour patterns of users for promising results in both domains. Time-aware Graph Attention mechanism (TGAT) is designed to learn the inter-domain sequence-level representation of items. Relationship-aware Graph Attention mechanism (RGAT) is proposed to learn collaborative items' and users' feature representations. Moreover, to simultaneously improve the recommendation performance in the two domains, a Cross-domain Feature Bidirectional Transfer module (CFBT) is proposed, transferring user's common sharing features in both domains and retaining user's domain-specific features in a specific domain. Finally, cross-domain and sequential information jointly recommend the next items that users like. We conduct extensive experiments on two real-world datasets that show that CsrGCF outperforms several state-of-the-art baselines in terms of Recall and MRR. These demonstrate the necessity of exploiting high-order behaviour patterns of users for a cross-domain sequential recommendation. Meanwhile, retaining domain-specific features is an important step in the process of cross-domain feature bidirectional transferring.http://dx.doi.org/10.1080/09540091.2022.2136141cross-domain sequential recommendationgraph-collaborative filteringdata sparsitygraph attention mechanism |
| spellingShingle | Bingyuan Wang Baisong Liu Hao Ren Xueyuan Zhang Jiangcheng Qin Qian Dong Jiangbo Qian Exploiting high-order behaviour patterns for cross-domain sequential recommendation Connection Science cross-domain sequential recommendation graph-collaborative filtering data sparsity graph attention mechanism |
| title | Exploiting high-order behaviour patterns for cross-domain sequential recommendation |
| title_full | Exploiting high-order behaviour patterns for cross-domain sequential recommendation |
| title_fullStr | Exploiting high-order behaviour patterns for cross-domain sequential recommendation |
| title_full_unstemmed | Exploiting high-order behaviour patterns for cross-domain sequential recommendation |
| title_short | Exploiting high-order behaviour patterns for cross-domain sequential recommendation |
| title_sort | exploiting high order behaviour patterns for cross domain sequential recommendation |
| topic | cross-domain sequential recommendation graph-collaborative filtering data sparsity graph attention mechanism |
| url | http://dx.doi.org/10.1080/09540091.2022.2136141 |
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