A Comprehensive Survey on Multimodal Recommender Systems: Taxonomy, Evaluation, and Future Directions
Hongyu Zhou, Xin Zhou, Zhiwei Zeng, Lingzi Zhang, Zhiqi Shen

TL;DR
This survey comprehensively reviews recent advancements in multimodal recommender systems, highlighting techniques, classifications, and providing a code framework to facilitate future research in understanding and implementing these models.
Contribution
It offers a detailed taxonomy, evaluation methods, and a practical code framework for multimodal recommendation models, aiding researchers in understanding and developing new approaches.
Findings
Classified models by methods used
Provided a comprehensive taxonomy and evaluation metrics
Developed a code framework for SOTA models
Abstract
Recommendation systems have become popular and effective tools to help users discover their interesting items by modeling the user preference and item property based on implicit interactions (e.g., purchasing and clicking). Humans perceive the world by processing the modality signals (e.g., audio, text and image), which inspired researchers to build a recommender system that can understand and interpret data from different modalities. Those models could capture the hidden relations between different modalities and possibly recover the complementary information which can not be captured by a uni-modal approach and implicit interactions. The goal of this survey is to provide a comprehensive review of the recent research efforts on the multimodal recommendation. Specifically, it shows a clear pipeline with commonly used techniques in each step and classifies the models by the methods used.…
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Taxonomy
TopicsRecommender Systems and Techniques · Music and Audio Processing · Advanced Text Analysis Techniques
