A Survey on Multimodal Recommender Systems: Recent Advances and Future Directions

Acquiring valuable data from the rapidly expanding information on the internet has become a significant concern, and recommender systems have emerged as a widely used and effective tool for helping users discover items of interest. The essence of recommender systems lies in their ability to predict users' ratings or preferences for various items and subsequently recommend the most relevant ones based on historical interaction data and publicly available information. With the advent of diverse multimedia services, including text, images, video, and audio, humans can perceive the world through multiple modalities. Consequently, a recommender system capable of understanding and interpreting different modal data can more effectively refer to individual preferences. Multimodal Recommender Systems (MRS) not only capture implicit interaction information across multiple modalities but also have the potential to uncover hidden relationships between these modalities. The primary objective of this survey is to comprehensively review recent research advancements in MRS and to analyze the models from a technical perspective. Specifically, we aim to summarize the general process and main challenges of MRS from a technical perspective. We then introduce the existing MRS models by categorizing them into four key areas: Feature Extraction, Encoder, Multimodal Fusion, and Loss Function. Finally, we further discuss potential future directions for developing and enhancing MRS. This survey serves as a comprehensive guide for researchers and practitioners in MRS field, providing insights into the current state of MRS technology and identifying areas for future research. We hope to contribute to developing a more sophisticated and effective multimodal recommender system. To access more details of this paper, we open source a repository: https://github.com/Jinfeng-Xu/Awesome-Multimodal-Recommender-Systems.