A survey that organizes fairness research in LLM-based recommender systems via a two-dimensional taxonomy of bias mechanisms and fairness targets while linking to other trustworthy AI concerns.
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cs.IR 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
SAILRec uses dual-side semantic alignment and hierarchical attention steering to improve how LLMs incorporate collaborative embeddings for recommendations, outperforming baselines on MovieLens-1M and Amazon-Book datasets.
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Rethinking Fairness in LLM-Based Recommender Systems: A Survey
A survey that organizes fairness research in LLM-based recommender systems via a two-dimensional taxonomy of bias mechanisms and fairness targets while linking to other trustworthy AI concerns.
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SAILRec: Steering LLM Attention to Dual-Side Semantically Aligned Collaborative Embeddings for Recommendation
SAILRec uses dual-side semantic alignment and hierarchical attention steering to improve how LLMs incorporate collaborative embeddings for recommendations, outperforming baselines on MovieLens-1M and Amazon-Book datasets.