LLM-driven Multimodal Recommendation

TL;DR

This paper introduces LMMRec, a framework that models user motivation explicitly using multimodal data, including review texts, to improve personalized recommendations and interpretability.

Contribution

It proposes a novel motivation-based recommendation framework that leverages multimodal data, especially review texts, to better capture user motivations and enhance recommendation quality.

Findings

LMMRec outperforms baseline models on three real-world datasets.

Explicit motivation modeling improves recommendation interpretability.

Incorporating review texts enhances the understanding of user preferences.

Abstract

As a paradigm that delves into the deep seated drivers of user behavior, motivation-based recommendation systems have emerged as a prominent research direction in the field of personalized information retrieval. Unlike traditional approaches that primarily rely on surface level interaction signals, these systems aim to uncover the intrinsic psychological factors that shape users' decision-making processes and content preferences. By modeling motivation, recommender systems can better interpret not only what users choose, but why they make such choices, thereby enhancing both the interpretability and the persuasive power of recommendations. However, existing studies often simplify motivation as a latent variable learned implicitly from behavioral data, which limits their ability to capture the semantic richness inherent in user motivations. In particular, heterogeneous information such as review texts which often carry explicit motivational cues remains underexplored in current motivation modeling frameworks. Extensive experiments conducted on three real world datasets demonstrate the effectiveness of the proposed LMMRec framework.