Technology Category
Machine Learning: Multimodal LearningData Mining & Knowledge Management: Recommender SystemsCognitive Modeling & Cognitive Systems: Adaptive Behavior
Joint Modeling in Recommendations: A Survey
🤖 AI Summary
Traditional recommender systems are constrained by single-task, single-scenario, single-modality, and single-behavior modeling, limiting their ability to capture users’ dynamic and complex preferences. To address this, we propose the first unified four-dimensional joint modeling paradigm—integrating multi-task learning, multi-scenario adaptation, multimodal fusion, and multi-behavior modeling. We systematically survey key techniques, including deep neural architectures, transfer learning, multi-source feature integration, behavioral sequence modeling, and cross-domain representation alignment, distilling common architectural principles and training strategies. Further, we construct a structured, knowledge-graph-inspired taxonomy that clarifies capability boundaries and applicability conditions of existing methods. For the first time, we deliver a reusable methodology guide and an open-problem checklist, bridging theoretical foundations with practical implementation. This work provides both principled guidance for algorithm design and actionable pathways for industrial deployment.
Technology Category
Application Category
📝 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.
Problem
Research questions and friction points this paper is trying to address.
Addresses limitations of single-task recommendation systems
Explores joint modeling for multi-task, multi-scenario recommendations
Surveys methods to enhance precision and customization in DRS
Innovation
Methods, ideas, or system contributions that make the work stand out.
Integrates multi-task, multi-scenario modeling techniques
Combines multi-modal and multi-behavior data analysis
Enhances recommendation precision and customization
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