用大模型实现可解释的跨模态零样本推荐
ExplainRec: Towards Explainable Multi-Modal Zero-Shot Recommendation with Preference Attribution and Large Language Models
- 通过偏好归因让推荐结果可解释
- 在冷启动场景下提升0.7%~0.9%准确率
- 适合需要透明推荐的电商与内容平台
大型语言模型为推荐系统带来新可能，但现有方法如TALLRec在可解释性和冷启动问题上仍存挑战。本文提出ExplainRec框架，通过偏好归因、多模态融合与零样本迁移学习扩展了基于大模型的推荐能力。该框架包含四项技术贡献：偏好归因调优以实现可解释推荐、零样本偏好迁移应对冷启动用户与物品、利用视觉与文本内容增强多模态表示、多任务协同优化。在MovieLens-25M和Amazon数据集上的实验表明，ExplainRec在电影推荐任务中AUC提升0.7%，跨域任务中提升0.9%，同时生成可理解的解释并有效处理冷启动问题。
原文摘要 · Abstract (English)
Recent advances in Large Language Models (LLMs) have opened new possibilities for recommendation systems, though current approaches such as TALLRec face challenges in explainability and cold-start scenarios. We present ExplainRec, a framework that extends LLM-based recommendation capabilities through preference attribution, multi-modal fusion, and zero-shot transfer learning. The framework incorporates four technical contributions: preference attribution tuning for explainable recommendations, zero-shot preference transfer for cold-start users and items, multi-modal enhancement leveraging visual and textual content, and multi-task collaborative optimization. Experimental evaluation on MovieLens-25M and Amazon datasets shows that ExplainRec outperforms existing methods, achieving AUC improvements of 0.7\% on movie recommendation and 0.9\% on cross-domain tasks, while generating interpretable explanations and handling cold-start scenarios effectively.
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