MMSRARec: Summarization and Retrieval Augumented Sequential Recommendation Based on Multimodal Large Language Model

TL;DR

This paper introduces MMSRARec, a multimodal recommendation system that uses large language models to summarize items, incorporate collaborative signals, and improve recommendation accuracy with interpretability and efficiency.

Contribution

The paper proposes a novel multimodal sequential recommendation framework that combines summarization, retrieval, and collaborative signals with fine-tuning of large language models.

Findings

Effective recommendation accuracy demonstrated on benchmark datasets.

Enhanced interpretability through item summarization.

Balanced computational cost with retrieval-augmented approach.

Abstract

Recent advancements in Multimodal Large Language Models (MLLMs) have demonstrated significant potential in recommendation systems. However, the effective application of MLLMs to multimodal sequential recommendation remains unexplored: A) Existing methods primarily leverage the multimodal semantic understanding capabilities of pre-trained MLLMs to generate item embeddings or semantic IDs, thereby enhancing traditional recommendation models. These approaches generate item representations that exhibit limited interpretability, and pose challenges when transferring to language model-based recommendation systems. B) Other approaches convert user behavior sequence into image-text pairs and perform recommendation through multiple MLLM inference, incurring prohibitive computational and time costs. C) Current MLLM-based recommendation systems generally neglect the integration of collaborative signals. To address these limitations while balancing recommendation performance, interpretability, and computational cost, this paper proposes MultiModal Summarization-and-Retrieval-Augmented Sequential Recommendation. Specifically, we first employ MLLM to summarize items into concise keywords and fine-tune the model using rewards that incorporate summary length, information loss, and reconstruction difficulty, thereby enabling adaptive adjustment of the summarization policy. Inspired by retrieval-augmented generation, we then transform collaborative signals into corresponding keywords and integrate them as supplementary context. Finally, we apply supervised fine-tuning with multi-task learning to align the MLLM with the multimodal sequential recommendation. Extensive evaluations on common recommendation datasets demonstrate the effectiveness of MMSRARec, showcasing its capability to efficiently and interpretably understand user behavior histories and item information for accurate recommendations.