@inproceedings{wu-etal-2026-sailrec,
title = "{SAILR}ec: Steering {LLM} Attention to Dual-Side Semantically Aligned Collaborative Embeddings for Recommendation",
author = "Wu, Xi and
Wang, Jiale and
Wang, Zihan and
Gao, Yichen and
Yang, Xiaocui and
Feng, Shi and
Wang, Daling and
Zhang, Yifei",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.1089/",
pages = "25013--25033",
ISBN = "979-8-89176-459-0",
abstract = "Recent LLM-based recommenders enhance language models with collaborative embeddings from user-item interactions, but making such embeddings available does not ensure their proper use during inference. Through a diagnostic attention analysis, we find that the utilization of collaborative embeddings is depth-dependent and alignment-sensitive, suggesting that LLMs need to balance their internal semantic knowledge with external collaborative knowledge. To address this issue, we propose SAILRec, an LLM-based recommender that improves this balance through dual-side semantic alignment and hierarchical attention steering. The former aligns item-side embeddings with item-text semantics and user-side embeddings with codebook-based semantic profiles, while the latter suppresses premature shallow-layer collaborative interference and strengthens collaborative evidence in deeper decision layers. Experiments on MovieLens-1M and Amazon-Book show that SAILRec consistently outperforms representative baselines, with ablation and masking analyses validating its key designs. Our implementation is available at https://github.com/XuPhoenixyear/SAILRec."
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<abstract>Recent LLM-based recommenders enhance language models with collaborative embeddings from user-item interactions, but making such embeddings available does not ensure their proper use during inference. Through a diagnostic attention analysis, we find that the utilization of collaborative embeddings is depth-dependent and alignment-sensitive, suggesting that LLMs need to balance their internal semantic knowledge with external collaborative knowledge. To address this issue, we propose SAILRec, an LLM-based recommender that improves this balance through dual-side semantic alignment and hierarchical attention steering. The former aligns item-side embeddings with item-text semantics and user-side embeddings with codebook-based semantic profiles, while the latter suppresses premature shallow-layer collaborative interference and strengthens collaborative evidence in deeper decision layers. Experiments on MovieLens-1M and Amazon-Book show that SAILRec consistently outperforms representative baselines, with ablation and masking analyses validating its key designs. Our implementation is available at https://github.com/XuPhoenixyear/SAILRec.</abstract>
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%0 Conference Proceedings
%T SAILRec: Steering LLM Attention to Dual-Side Semantically Aligned Collaborative Embeddings for Recommendation
%A Wu, Xi
%A Wang, Jiale
%A Wang, Zihan
%A Gao, Yichen
%A Yang, Xiaocui
%A Feng, Shi
%A Wang, Daling
%A Zhang, Yifei
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F wu-etal-2026-sailrec
%X Recent LLM-based recommenders enhance language models with collaborative embeddings from user-item interactions, but making such embeddings available does not ensure their proper use during inference. Through a diagnostic attention analysis, we find that the utilization of collaborative embeddings is depth-dependent and alignment-sensitive, suggesting that LLMs need to balance their internal semantic knowledge with external collaborative knowledge. To address this issue, we propose SAILRec, an LLM-based recommender that improves this balance through dual-side semantic alignment and hierarchical attention steering. The former aligns item-side embeddings with item-text semantics and user-side embeddings with codebook-based semantic profiles, while the latter suppresses premature shallow-layer collaborative interference and strengthens collaborative evidence in deeper decision layers. Experiments on MovieLens-1M and Amazon-Book show that SAILRec consistently outperforms representative baselines, with ablation and masking analyses validating its key designs. Our implementation is available at https://github.com/XuPhoenixyear/SAILRec.
%U https://aclanthology.org/2026.emnlp-main.1089/
%P 25013-25033
Markdown (Informal)
[SAILRec: Steering LLM Attention to Dual-Side Semantically Aligned Collaborative Embeddings for Recommendation](https://aclanthology.org/2026.emnlp-main.1089/) (Wu et al., EMNLP 2026)
ACL
- Xi Wu, Jiale Wang, Zihan Wang, Yichen Gao, Xiaocui Yang, Shi Feng, Daling Wang, and Yifei Zhang. 2026. SAILRec: Steering LLM Attention to Dual-Side Semantically Aligned Collaborative Embeddings for Recommendation. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 25013–25033, Budapest, Hungary. Association for Computational Linguistics.