Sequential recommendation algorithm for long-tail users based on knowledge-enhanced contrastive learning
Sequential recommendation predicts next items for users based on their historical interactions. Existing methods capture long-term dependencies but struggle to recommend precisely for users with short interaction sequences, especially for long-tail users. Therefore, a sequential recommendation algor...
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| Format: | Article |
| Language: | zho |
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Editorial Department of Journal on Communications
2024-06-01
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| Series: | Tongxin xuebao |
| Subjects: | |
| Online Access: | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2024107/ |
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| author | REN Yonggong ZHOU Pinglei ZHANG Zhipeng |
| author_facet | REN Yonggong ZHOU Pinglei ZHANG Zhipeng |
| author_sort | REN Yonggong |
| collection | DOAJ |
| description | Sequential recommendation predicts next items for users based on their historical interactions. Existing methods capture long-term dependencies but struggle to recommend precisely for users with short interaction sequences, especially for long-tail users. Therefore, a sequential recommendation algorithm for long-tail users based on knowledge-enhanced contrastive learning was proposed. Firstly, semantic item similarity was introduced by leveraging relationships between entities in the knowledge graph to extract correlated items from original sequences. Secondly, two sequence augmentation operators were proposed based on different contrastive learning views, addressing the problem of insufficient training for long-tail user sequences by augmenting self-supervised signals. Finally, precise sequence recommendations were provided for long-tail users by utilizing the joint training of shared network parameters between contrastive self-supervised tasks and the recommendation task. Experimental results on real-world datasets demonstrate the effectiveness of the proposed algorithm in improving performance for long-tail users. |
| format | Article |
| id | doaj-art-220d7bf0904d411a91eb9aa3acb16f54 |
| institution | Kabale University |
| issn | 1000-436X |
| language | zho |
| publishDate | 2024-06-01 |
| publisher | Editorial Department of Journal on Communications |
| record_format | Article |
| series | Tongxin xuebao |
| spelling | doaj-art-220d7bf0904d411a91eb9aa3acb16f542025-01-14T07:24:32ZzhoEditorial Department of Journal on CommunicationsTongxin xuebao1000-436X2024-06-014521022263977233Sequential recommendation algorithm for long-tail users based on knowledge-enhanced contrastive learningREN YonggongZHOU PingleiZHANG ZhipengSequential recommendation predicts next items for users based on their historical interactions. Existing methods capture long-term dependencies but struggle to recommend precisely for users with short interaction sequences, especially for long-tail users. Therefore, a sequential recommendation algorithm for long-tail users based on knowledge-enhanced contrastive learning was proposed. Firstly, semantic item similarity was introduced by leveraging relationships between entities in the knowledge graph to extract correlated items from original sequences. Secondly, two sequence augmentation operators were proposed based on different contrastive learning views, addressing the problem of insufficient training for long-tail user sequences by augmenting self-supervised signals. Finally, precise sequence recommendations were provided for long-tail users by utilizing the joint training of shared network parameters between contrastive self-supervised tasks and the recommendation task. Experimental results on real-world datasets demonstrate the effectiveness of the proposed algorithm in improving performance for long-tail users.http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2024107/sequential recommendationlong-tail userknowledge graphcontrastive learning |
| spellingShingle | REN Yonggong ZHOU Pinglei ZHANG Zhipeng Sequential recommendation algorithm for long-tail users based on knowledge-enhanced contrastive learning Tongxin xuebao sequential recommendation long-tail user knowledge graph contrastive learning |
| title | Sequential recommendation algorithm for long-tail users based on knowledge-enhanced contrastive learning |
| title_full | Sequential recommendation algorithm for long-tail users based on knowledge-enhanced contrastive learning |
| title_fullStr | Sequential recommendation algorithm for long-tail users based on knowledge-enhanced contrastive learning |
| title_full_unstemmed | Sequential recommendation algorithm for long-tail users based on knowledge-enhanced contrastive learning |
| title_short | Sequential recommendation algorithm for long-tail users based on knowledge-enhanced contrastive learning |
| title_sort | sequential recommendation algorithm for long tail users based on knowledge enhanced contrastive learning |
| topic | sequential recommendation long-tail user knowledge graph contrastive learning |
| url | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2024107/ |
| work_keys_str_mv | AT renyonggong sequentialrecommendationalgorithmforlongtailusersbasedonknowledgeenhancedcontrastivelearning AT zhoupinglei sequentialrecommendationalgorithmforlongtailusersbasedonknowledgeenhancedcontrastivelearning AT zhangzhipeng sequentialrecommendationalgorithmforlongtailusersbasedonknowledgeenhancedcontrastivelearning |