Generating Negative Samples for Multi-Modal Recommendation

TL;DR

This paper introduces NegGen, a framework that uses multi-modal large language models to generate balanced, contrastive negative samples, improving multi-modal recommendation systems by addressing key negative sampling challenges.

Contribution

NegGen is the first to leverage multi-modal large language models for generating effective negative samples in multi-modal recommender systems, addressing modality balance and contrastiveness.

Findings

NegGen outperforms state-of-the-art negative sampling methods.

NegGen improves recommendation accuracy and diversity.

NegGen effectively maintains modality balance in negative samples.

Abstract

Multi-modal recommender systems (MMRS) have gained significant attention due to their ability to leverage information from various modalities to enhance recommendation quality. However, existing negative sampling techniques often struggle to effectively utilize the multi-modal data, leading to suboptimal performance. In this paper, we identify two key challenges in negative sampling for MMRS: (1) producing cohesive negative samples contrasting with positive samples and (2) maintaining a balanced influence across different modalities. To address these challenges, we propose NegGen, a novel framework that utilizes multi-modal large language models (MLLMs) to generate balanced and contrastive negative samples. We design three different prompt templates to enable NegGen to analyze and manipulate item attributes across multiple modalities, and then generate negative samples that introduce better supervision signals and ensure modality balance. Furthermore, NegGen employs a causal learning module to disentangle the effect of intervened key features and irrelevant item attributes, enabling fine-grained learning of user preferences. Extensive experiments on real-world datasets demonstrate the superior performance of NegGen compared to state-of-the-art methods in both negative sampling and multi-modal recommendation.