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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| Main Authors: | , , |
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| Format: | Article |
| Language: | zho |
| Published: |
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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