Joint Modeling in Recommendations: A Survey
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arXiv
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| Main Authors: | , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866910850918383616 |
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| author | Zhao, Xiangyu Wang, Yichao Chen, Bo Gao, Jingtong Wang, Yuhao Li, Xiaopeng Jia, Pengyue Liu, Qidong Guo, Huifeng Tang, Ruiming |
| author_facet | Zhao, Xiangyu Wang, Yichao Chen, Bo Gao, Jingtong Wang, Yuhao Li, Xiaopeng Jia, Pengyue Liu, Qidong Guo, Huifeng Tang, Ruiming |
| contents | In today's digital landscape, Deep Recommender Systems (DRS) play a crucial role in navigating and customizing online content for individual preferences. However, conventional methods, which mainly depend on single recommendation task, scenario, data modality and user behavior, are increasingly seen as insufficient due to their inability to accurately reflect users' complex and changing preferences. This gap underscores the need for joint modeling approaches, which are central to overcoming these limitations by integrating diverse tasks, scenarios, modalities, and behaviors in the recommendation process, thus promising significant enhancements in recommendation precision, efficiency, and customization. In this paper, we comprehensively survey the joint modeling methods in recommendations. We begin by defining the scope of joint modeling through four distinct dimensions: multi-task, multi-scenario, multi-modal, and multi-behavior modeling. Subsequently, we examine these methods in depth, identifying and summarizing their underlying paradigms based on the latest advancements and potential research trajectories. Ultimately, we highlight several promising avenues for future exploration in joint modeling for recommendations and provide a concise conclusion to our findings. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_21195 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Joint Modeling in Recommendations: A Survey Zhao, Xiangyu Wang, Yichao Chen, Bo Gao, Jingtong Wang, Yuhao Li, Xiaopeng Jia, Pengyue Liu, Qidong Guo, Huifeng Tang, Ruiming Information Retrieval In today's digital landscape, Deep Recommender Systems (DRS) play a crucial role in navigating and customizing online content for individual preferences. However, conventional methods, which mainly depend on single recommendation task, scenario, data modality and user behavior, are increasingly seen as insufficient due to their inability to accurately reflect users' complex and changing preferences. This gap underscores the need for joint modeling approaches, which are central to overcoming these limitations by integrating diverse tasks, scenarios, modalities, and behaviors in the recommendation process, thus promising significant enhancements in recommendation precision, efficiency, and customization. In this paper, we comprehensively survey the joint modeling methods in recommendations. We begin by defining the scope of joint modeling through four distinct dimensions: multi-task, multi-scenario, multi-modal, and multi-behavior modeling. Subsequently, we examine these methods in depth, identifying and summarizing their underlying paradigms based on the latest advancements and potential research trajectories. Ultimately, we highlight several promising avenues for future exploration in joint modeling for recommendations and provide a concise conclusion to our findings. |
| title | Joint Modeling in Recommendations: A Survey |
| topic | Information Retrieval |
| url | https://arxiv.org/abs/2502.21195 |