SAILRec: Steering LLM Attention to Dual-Side Semantically Aligned Collaborative Embeddings for Recommendation
2026-06-03 • Information Retrieval
Information Retrieval
AI summaryⓘ
The authors found that simply adding user-item interaction data to language models doesn't guarantee the models use this information properly when recommending items. They discovered that the way the model uses this data depends on the layer depth and how well the data aligns with the model's own knowledge. To fix this, they created SAILRec, which makes the model balance its own understanding with external user-item information by aligning embeddings and controlling attention layers. Their tests showed SAILRec worked better than other methods, confirming their approach was effective.
LLM-based recommendercollaborative embeddingsuser-item interactionssemantic alignmenthierarchical attentionMovieLens-1MAmazon-Book datasetattention analysisablation study
Authors
Xi Wu, Jiale Wang, Zihan Wang, Yichen Gao, Xiaocui Yang, Shi Feng, Daling Wang, Yifei Zhang
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.