Multi-Behavior Recommender Systems: A Survey
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866909532521758720 |
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| author | Kim, Kyungho Kim, Sunwoo Lee, Geon Jung, Jinhong Shin, Kijung |
| author_facet | Kim, Kyungho Kim, Sunwoo Lee, Geon Jung, Jinhong Shin, Kijung |
| contents | Traditional recommender systems primarily rely on a single type of user-item interaction, such as item purchases or ratings, to predict user preferences. However, in real-world scenarios, users engage in a variety of behaviors, such as clicking on items or adding them to carts, offering richer insights into their interests. Multi-behavior recommender systems leverage these diverse interactions to enhance recommendation quality, and research on this topic has grown rapidly in recent years. This survey provides a timely review of multi-behavior recommender systems, focusing on three key steps: (1) Data Modeling: representing multi-behaviors at the input level, (2) Encoding: transforming these inputs into vector representations (i.e., embeddings), and (3) Training: optimizing machine-learning models. We systematically categorize existing multi-behavior recommender systems based on the commonalities and differences in their approaches across the above steps. Additionally, we discuss promising future directions for advancing multi-behavior recommender systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_06963 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Multi-Behavior Recommender Systems: A Survey Kim, Kyungho Kim, Sunwoo Lee, Geon Jung, Jinhong Shin, Kijung Information Retrieval Artificial Intelligence Traditional recommender systems primarily rely on a single type of user-item interaction, such as item purchases or ratings, to predict user preferences. However, in real-world scenarios, users engage in a variety of behaviors, such as clicking on items or adding them to carts, offering richer insights into their interests. Multi-behavior recommender systems leverage these diverse interactions to enhance recommendation quality, and research on this topic has grown rapidly in recent years. This survey provides a timely review of multi-behavior recommender systems, focusing on three key steps: (1) Data Modeling: representing multi-behaviors at the input level, (2) Encoding: transforming these inputs into vector representations (i.e., embeddings), and (3) Training: optimizing machine-learning models. We systematically categorize existing multi-behavior recommender systems based on the commonalities and differences in their approaches across the above steps. Additionally, we discuss promising future directions for advancing multi-behavior recommender systems. |
| title | Multi-Behavior Recommender Systems: A Survey |
| topic | Information Retrieval Artificial Intelligence |
| url | https://arxiv.org/abs/2503.06963 |