IRA: Adaptive Interest-aware Representation and Alignment for Personalized Multi-interest Retrieval
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arXiv
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| Autori principali: | , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866913822278680576 |
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| author | Lee, Youngjune Jeong, Haeyu Lim, Changgeon Choi, Jeong Lim, Hongjun Kim, Hangon Kwon, Jiyoon Kim, Saehun |
| author_facet | Lee, Youngjune Jeong, Haeyu Lim, Changgeon Choi, Jeong Lim, Hongjun Kim, Hangon Kwon, Jiyoon Kim, Saehun |
| contents | Online community platforms require dynamic personalized retrieval and recommendation that can continuously adapt to evolving user interests and new documents. However, optimizing models to handle such changes in real-time remains a major challenge in large-scale industrial settings. To address this, we propose the Interest-aware Representation and Alignment (IRA) framework, an efficient and scalable approach that dynamically adapts to new interactions through a cumulative structure. IRA leverages two key mechanisms: (1) Interest Units that capture diverse user interests as contextual texts, while reinforcing or fading over time through cumulative updates, and (2) a retrieval process that measures the relevance between Interest Units and documents based solely on semantic relationships, eliminating dependence on click signals to mitigate temporal biases. By integrating cumulative Interest Unit updates with the retrieval process, IRA continuously adapts to evolving user preferences, ensuring robust and fine-grained personalization without being constrained by past training distributions. We validate the effectiveness of IRA through extensive experiments on real-world datasets, including its deployment in the Home Section of NAVER's CAFE, South Korea's leading community platform. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_17529 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | IRA: Adaptive Interest-aware Representation and Alignment for Personalized Multi-interest Retrieval Lee, Youngjune Jeong, Haeyu Lim, Changgeon Choi, Jeong Lim, Hongjun Kim, Hangon Kwon, Jiyoon Kim, Saehun Information Retrieval Machine Learning Online community platforms require dynamic personalized retrieval and recommendation that can continuously adapt to evolving user interests and new documents. However, optimizing models to handle such changes in real-time remains a major challenge in large-scale industrial settings. To address this, we propose the Interest-aware Representation and Alignment (IRA) framework, an efficient and scalable approach that dynamically adapts to new interactions through a cumulative structure. IRA leverages two key mechanisms: (1) Interest Units that capture diverse user interests as contextual texts, while reinforcing or fading over time through cumulative updates, and (2) a retrieval process that measures the relevance between Interest Units and documents based solely on semantic relationships, eliminating dependence on click signals to mitigate temporal biases. By integrating cumulative Interest Unit updates with the retrieval process, IRA continuously adapts to evolving user preferences, ensuring robust and fine-grained personalization without being constrained by past training distributions. We validate the effectiveness of IRA through extensive experiments on real-world datasets, including its deployment in the Home Section of NAVER's CAFE, South Korea's leading community platform. |
| title | IRA: Adaptive Interest-aware Representation and Alignment for Personalized Multi-interest Retrieval |
| topic | Information Retrieval Machine Learning |
| url | https://arxiv.org/abs/2504.17529 |