Multi-Granularity Modeling in Recommendation: from the Multi-Scenario Perspective

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 multi-granularity modeling, 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, from the multi-scenario perspective, we illustrate our existing explorations and present results. Ultimately, we wish to highlight our multi-granularity approach sheds light on building the next generation of recommender system1 .