用大模型提升多模态推荐的准确与公平性
LLM4Rec: Large Language Models for Multimodal Generative Recommendation with Causal Debiasing
- 融合多模态信息并用因果分析消除推荐偏见
- 在三个数据集上实现最高2.3%的NDCG@10提升
- 适合关注可解释性与公平推荐的研究者
当前生成式推荐系统在处理多模态数据、消除算法偏见和提供透明决策方面面临挑战。本文提出一个增强型生成推荐框架，包含五项关键创新：多模态融合架构、检索增强生成机制、基于因果推断的去偏方法、可解释推荐生成以及实时自适应学习能力。该框架以先进大语言模型为骨干，集成跨模态理解、上下文知识融合、偏见缓解、解释合成与持续模型适应等专用模块。在MovieLens-25M、Amazon-Electronics、Yelp-2023三个基准数据集上的实验表明，相比现有方法，该框架在推荐准确率、公平性和多样性上均有显著提升。在保持计算效率的同时，实现了高达2.3%的NDCG@10提升和1.4%的多样性指标优化。
原文摘要 · Abstract (English)
Contemporary generative recommendation systems face significant challenges in handling multimodal data, eliminating algorithmic biases, and providing transparent decision-making processes. This paper introduces an enhanced generative recommendation framework that addresses these limitations through five key innovations: multimodal fusion architecture, retrieval-augmented generation mechanisms, causal inference-based debiasing, explainable recommendation generation, and real-time adaptive learning capabilities. Our framework leverages advanced large language models as the backbone while incorporating specialized modules for cross-modal understanding, contextual knowledge integration, bias mitigation, explanation synthesis, and continuous model adaptation. Extensive experiments on three benchmark datasets (MovieLens-25M, Amazon-Electronics, Yelp-2023) demonstrate consistent improvements in recommendation accuracy, fairness, and diversity compared to existing approaches. The proposed framework achieves up to 2.3% improvement in NDCG@10 and 1.4% enhancement in diversity metrics while maintaining computational efficiency through optimized inference strategies.
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