Multimodal-aware Multi-intention Learning for Recommendation

Whether it is an e-commerce platform or a short video platform, the effective use of multi-modal data plays an important role in the recommendation system. More and more researchers are exploring how to effectively use multimodal signals to entice more users to buy goods or watch short videos. Some studies have added multimodal features as side information to the model and achieved certain results. In practice, the purchase behavior of users mainly depends on some personalized intentions. However, it is difficult for neural networks to process noise information and extract high-level intention information effectively. To investigate the benefits of latent intentions and leverage them effectively for recommendation, we propose a Multimodal-aware Multi-intention Learning method for recommendation (MMIL). Specifically, we first construct a multi-intention recommendation framework based on the probability distribution relationship among predicted objectives and intentions, while avoiding intention overfitting. We then design an intention representation learning module to learn accurate multiple intention representations based on intention prototypes. Further, we propose a multi-modal intention perceptron module to learn multi-modal intention representations. In addition, we design self-supervised intention-based and representation-based contrastive objectives to achieve cross-modal representation alignment. On three real-world data sets, the proposed MMIL method outperforms other advanced techniques. The effectiveness of intention modeling and intention alignment is verified by comprehensive experiments. The source code is available at: https://github.com/ml-mindset/MMIL.