Joint Modeling in Recommendations: A Survey

TL;DR

This survey reviews joint modeling techniques in deep recommender systems, emphasizing their role in integrating multiple tasks, scenarios, modalities, and behaviors to improve recommendation accuracy and personalization.

Contribution

It systematically categorizes and analyzes recent joint modeling approaches in recommendation systems across four key dimensions, highlighting future research directions.

Findings

Joint modeling enhances recommendation precision and personalization.

Four dimensions define the scope of joint modeling: multi-task, multi-scenario, multi-modal, multi-behavior.

The survey identifies promising future research avenues in joint recommendation modeling.

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.