Indirect Interactions Discovering and True Negative Sampling for Multimodal Recommendation

Multimodal recommendation has become a key technology for social media platforms. It is widely used in content recommendation, user preference analysis, advertisement placement, etc. Existing recommendation methods mainly focus on learning multimodal embeddings from direct interactions between users and items, ignoring indirect interactions among users-to-users and items-to-items. This limits the further exploration of potential interests between users and items. Moreover, during the model training, classical recommendation methods usually randomly select uninteracted items of a user as their negative samples. This may introduce significant learning bias, as uninteracted items could be false negatives and still potentially interest the user. To this end, we propose a novel indirect interactions discovery and true negative sampling multimodal recommendation (ITMRec) method to further explore potential user interests and mitigate the issue of false negative samples during learning. Specifically, we propose an indirect interactions discovering (IID) model to explore the latent interests among users-to-users and items-to-items. Then, we propose a true negative sampling (TNS) model to refine negative sampling that can alleviate the false negative sample problem. Finally, we enhance existing collaborative filtering methods by integrating representations derived from multimodal content, indirect interactions discovery, and refined negative sampling strategies, allowing for more precise alignment with users’ latent interests. Extensive experiments on three benchmark datasets demonstrate that our ITMRec significantly outperforms state-of-the-art recommendation baselines, achieving a 3.64% improvement over peer methods. The code is available at https://github.com/long-best/ITMRec.git.