@inproceedings{zhao-etal-2025-motir,
title = "{M}oti{R}: Motivation-aware Retrieval for Long-Tail Recommendation",
author = "Zhao, Kaichen and
Li, Mingming and
Zhao, Haiquan and
Liu, Kuien and
Li, Zhixu and
Li, Xueying",
editor = "Rehm, Georg and
Li, Yunyao",
booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 6: Industry Track)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.acl-industry.65/",
doi = "10.18653/v1/2025.acl-industry.65",
pages = "934--945",
ISBN = "979-8-89176-288-6",
abstract = "In the retrieval stage of recommendation systems, two-tower models are widely adopted for their efficiency as a predominant paradigm. However, this method, which relies on collaborative filtering signals, exhibits limitations in modeling similarity for long-tail items. To address this issue, we propose a Motivation-aware Retrieval for Long-Tail Recommendation, named MotiR. The purchase motivations generated by LLMs represent a condensed abstraction of items' intrinsic attributes. By effectively integrating them with traditional item features, this approach enables the two-tower model to capture semantic-level similarities among long-tail items. Furthermore, a gated network-based adaptive weighting mechanism dynamically adjusts representation weights: emphasizing semantic modeling for long-tail items while preserving collaborative signal advantages for popular items. Experimental results demonstrate 60.5{\%} Hit@10 improvements over existing methods on Amazon Books. Industrial deployment in Taobao{\&}Tmall Group 88VIP scenarios achieves over 4{\%} CTR and CVR improvement, validating the effectiveness of our method."
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%0 Conference Proceedings
%T MotiR: Motivation-aware Retrieval for Long-Tail Recommendation
%A Zhao, Kaichen
%A Li, Mingming
%A Zhao, Haiquan
%A Liu, Kuien
%A Li, Zhixu
%A Li, Xueying
%Y Rehm, Georg
%Y Li, Yunyao
%S Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 6: Industry Track)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-288-6
%F zhao-etal-2025-motir
%X In the retrieval stage of recommendation systems, two-tower models are widely adopted for their efficiency as a predominant paradigm. However, this method, which relies on collaborative filtering signals, exhibits limitations in modeling similarity for long-tail items. To address this issue, we propose a Motivation-aware Retrieval for Long-Tail Recommendation, named MotiR. The purchase motivations generated by LLMs represent a condensed abstraction of items’ intrinsic attributes. By effectively integrating them with traditional item features, this approach enables the two-tower model to capture semantic-level similarities among long-tail items. Furthermore, a gated network-based adaptive weighting mechanism dynamically adjusts representation weights: emphasizing semantic modeling for long-tail items while preserving collaborative signal advantages for popular items. Experimental results demonstrate 60.5% Hit@10 improvements over existing methods on Amazon Books. Industrial deployment in Taobao&Tmall Group 88VIP scenarios achieves over 4% CTR and CVR improvement, validating the effectiveness of our method.
%R 10.18653/v1/2025.acl-industry.65
%U https://aclanthology.org/2025.acl-industry.65/
%U https://doi.org/10.18653/v1/2025.acl-industry.65
%P 934-945
Markdown (Informal)
[MotiR: Motivation-aware Retrieval for Long-Tail Recommendation](https://aclanthology.org/2025.acl-industry.65/) (Zhao et al., ACL 2025)
ACL
- Kaichen Zhao, Mingming Li, Haiquan Zhao, Kuien Liu, Zhixu Li, and Xueying Li. 2025. MotiR: Motivation-aware Retrieval for Long-Tail Recommendation. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 6: Industry Track), pages 934–945, Vienna, Austria. Association for Computational Linguistics.