ClueRec: Learning User Preference Evidence for Recommendation

Large language models (LLMs) have enhanced recommendation by bringing rich semantic knowledge into user and item modeling. However, existing LLM-based recommendation efforts mainly learn from positive interactions while overlooking the alternatives shown alongside each clicked item. Because a click is made within an exposure set, differences between the clicked item and its co-exposed alternatives provide valuable evidence about the user's relative preferences. Ignoring such evidence allows models to learn what users choose, but not why one item is preferred over another. To address this limitation, we propose **ClueRec**, an LLM-based recommendation model that explicitly learns preference evidence from exposure contexts. ClueRec comprises a Semantic Mapper, a Preference Evidence Miner and a Recommender. Its Semantic Mapper encodes multimodal item content into coarse-to-fine Semantic IDs (SIDs) and aligns the newly introduced SID tokens with the LLM's semantic space. The Preference Evidence Miner organizes clicked and co-exposed items into SID trees, extracts gated multi-level evidence from their divergent branches, and progressively injects this evidence into the LLM through attention. The Recommender utilizes this evidence-enhanced user representation to support both generative recommendation and CTR prediction. Extensive offline experiments on large-scale industrial data show that ClueRec consistently outperforms strong baselines on both generative recommendation and CTR prediction. Online A/B testing in a real-world advertising scenario further demonstrates stable improvements in user engagement and platform revenue. Our anonymous code is available at https://anonymous.4open.science/r/ClueRec-2E8B.