Joint Modeling in Recommendations: A Survey
Journal:
arXiv
Published Date:
Feb 28, 2025
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.
