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A Comprehensive Survey on Multimodal Recommender Systems: Taxonomy, Evaluation, and Future Directions.

Hongyu Zhou, Xin Zhou, Zhiwei Zeng

IEEE Transactions on Pattern Analysis and Machine Intelligence

|July 6, 2026

Summary

This survey reviews multimodal recommendation systems, which use diverse data types to enhance personalization. It provides a framework and analysis to guide future research in this evolving field.

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Area of Science:

Artificial Intelligence
Computer Science
Information Retrieval

Background:

Recommender systems personalize user experiences using historical interaction data.
Multimodal recommendation systems leverage diverse data types (e.g., text, images, audio) for improved accuracy.
Existing systems often overlook complementary information present across different modalities.

Purpose of the Study:

To provide a comprehensive review of recent research in multimodal recommendation.
To delineate a standard pipeline, outline techniques, and classify existing models.
To offer a performance analysis of baseline models and suggest future research directions.

Main Methods:

Systematic literature review of multimodal recommendation research.
Analysis of common pipeline stages and techniques used in multimodal systems.
Performance evaluation of selected baseline models and development of a code framework.

Main Results:

Identification of a common pipeline structure in multimodal recommendation systems.
Classification of models based on their underlying methodologies.
Performance insights into baseline models, highlighting factors influencing effectiveness.

Conclusions:

Multimodal recommendation offers significant potential for enhanced personalization.
The developed code framework facilitates understanding and implementation of state-of-the-art models.
Open issues and future research directions in multimodal recommendation are identified.