SAERec extracts fine-grained interpretable intents from LLM embeddings via sparse autoencoders and integrates them as priors into sequence recommendation using multi-branch attention, outperforming baselines on public datasets.
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SAERec: Constructing Fine-grained Interpretable Intents Priors via Sparse Autoencoders for Recommendation
SAERec extracts fine-grained interpretable intents from LLM embeddings via sparse autoencoders and integrates them as priors into sequence recommendation using multi-branch attention, outperforming baselines on public datasets.