LLM4Rec: Large Language Models for Multimodal Generative Recommendation with Causal Debiasing

• URL: http://arxiv.org/abs/2510.01622v1
• Date: Thu, 02 Oct 2025 02:53:05 GMT
• Title: LLM4Rec: Large Language Models for Multimodal Generative Recommendation with Causal Debiasing
• Authors: Bo Ma, Hang Li, ZeHua Hu, XiaoFan Gui, LuYao Liu, Simon Lau,
• Abstract summary: This paper introduces an enhanced generative recommendation framework with five key innovations.<n> multimodal fusion architecture, retrieval-augmented generation mechanisms, causal inference-based debiasing, explainable recommendation generation, and real-time adaptive learning capabilities.
• Score: 4.638507244153875
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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