Multi-Item-Query Attention for Stable Sequential Recommendation
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866908565293236224 |
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| author | Xu, Mingshi Zhu, Haoren Ng, Wilfred Siu Hung |
| author_facet | Xu, Mingshi Zhu, Haoren Ng, Wilfred Siu Hung |
| contents | The inherent instability and noise in user interaction data challenge sequential recommendation systems. Prevailing masked attention models, relying on a single query from the most recent item, are sensitive to this noise, reducing prediction reliability. We propose the Multi-Item-Query attention mechanism (MIQ-Attn) to enhance model stability and accuracy. MIQ-Attn constructs multiple diverse query vectors from user interactions, effectively mitigating noise and improving consistency. It is designed for easy adoption as a drop-in replacement for existing single-query attention. Experiments show MIQ-Attn significantly improves performance on benchmark datasets. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_24424 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Multi-Item-Query Attention for Stable Sequential Recommendation Xu, Mingshi Zhu, Haoren Ng, Wilfred Siu Hung Information Retrieval Artificial Intelligence Machine Learning The inherent instability and noise in user interaction data challenge sequential recommendation systems. Prevailing masked attention models, relying on a single query from the most recent item, are sensitive to this noise, reducing prediction reliability. We propose the Multi-Item-Query attention mechanism (MIQ-Attn) to enhance model stability and accuracy. MIQ-Attn constructs multiple diverse query vectors from user interactions, effectively mitigating noise and improving consistency. It is designed for easy adoption as a drop-in replacement for existing single-query attention. Experiments show MIQ-Attn significantly improves performance on benchmark datasets. |
| title | Multi-Item-Query Attention for Stable Sequential Recommendation |
| topic | Information Retrieval Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2509.24424 |