# LLM-driven Multimodal Recommendation

arXiv: https://arxiv.org/abs/2602.05474 · HTML: https://mlsanity.com/paper/2602.05474/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 18 of its 51 references that are in the map.
- First posted on arXiv 5 Feb 2026.

## Builds on (most cited first, 18 of 18)

- [Controllable Multi-Interest Framework for Recommendation](https://mlsanity.com/paper/2005.09347/) (2020)
- [ONCE: Boosting Content-based Recommendation with Both Open- and Closed-source Large Language Models](https://mlsanity.com/paper/2305.06566/) (2023)
- [Federated Unlearning for On-Device Recommendation](https://mlsanity.com/paper/2210.10958/) (2022)
- [LLM-Powered User Simulator for Recommender System](https://mlsanity.com/paper/2412.16984/) (2024)
- [ArtBank: Artistic Style Transfer with Pre-trained Diffusion Model and Implicit Style Prompt Bank](https://mlsanity.com/paper/2312.06135/) (2023)
- [HeteFedRec: Federated Recommender Systems with Model Heterogeneity](https://mlsanity.com/paper/2307.12810/) (2023)
- [Towards Highly Realistic Artistic Style Transfer via Stable Diffusion with Step-aware and Layer-aware Prompt](https://mlsanity.com/paper/2404.11474/) (2024)
- [Hide Your Model: A Parameter Transmission-free Federated Recommender System](https://mlsanity.com/paper/2311.14968/) (2023)
- [FELLAS: Enhancing Federated Sequential Recommendation with LLM as External Services](https://mlsanity.com/paper/2410.04927/) (2024)
- [An Automatic Graph Construction Framework based on Large Language Models for Recommendation](https://mlsanity.com/paper/2412.18241/) (2024)
- [Data Augmentation as Free Lunch: Exploring the Test-Time Augmentation for Sequential Recommendation](https://mlsanity.com/paper/2504.04843/) (2025)
- [Discrete Semantic Tokenization for Deep CTR Prediction](https://mlsanity.com/paper/2403.08206/) (2024)
- [Mitigating Propensity Bias of Large Language Models for Recommender Systems](https://mlsanity.com/paper/2409.20052/) (2024)
- [PolyCF: Towards the Optimal Spectral Graph Filters for Collaborative Filtering](https://mlsanity.com/paper/2401.12590/) (2024)
- [Disentangling ID and Modality Effects for Session-based Recommendation](https://mlsanity.com/paper/2404.12969/) (2024)
- [PTF-FSR: A Parameter Transmission-Free Federated Sequential Recommender System](https://mlsanity.com/paper/2406.05387/) (2024)
- [Squeeze and Excitation: A Weighted Graph Contrastive Learning for Collaborative Filtering](https://mlsanity.com/paper/2504.04443/) (2025)
- [StyMam: A Mamba-Based Generator for Artistic Style Transfer](https://mlsanity.com/paper/2601.12954/) (2026)

## Abstract (arXiv)

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.