Joint Modeling in Recommendations: A Survey

• URL: http://arxiv.org/abs/2502.21195v1
• Date: Fri, 28 Feb 2025 16:14:00 GMT
• Title: Joint Modeling in Recommendations: A Survey
• Authors: Xiangyu Zhao, Yichao Wang, Bo Chen, Jingtong Gao, Yuhao Wang, Xiaopeng Li, Pengyue Jia, Qidong Liu, Huifeng Guo, Ruiming Tang,
• Abstract summary: Joint modeling approaches are central to overcoming limitations by integrating diverse tasks, scenarios, modalities, and behaviors in the recommendation process.<n>We define the scope of joint modeling through four distinct dimensions: multi-task, multi-scenario, multi-modal, and multi-behavior modeling.<n>We highlight several promising avenues for future exploration in joint modeling for recommendations and provide a concise conclusion to our findings.
• Score: 46.000357352884926
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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