Year: 2025
Type: conference-paper
Abstract: In the current digital era, Deep Recommender Systems (DRS) are essential for navigating and tailoring online content to individual preferences. However, conventional approaches that rely primarily on a single recommendation task, scenario, data modality, or user behavior are increasingly inadequate for capturing users' complex and evolving preferences. This limitation highlights the need for joint modeling approaches that integrate multiple tasks, scenarios, modalities, and behaviors within the recommendation process, enhancing recommendation precision, efficiency, and personalization. In this tutorial, we aim to give a comprehensive survey on the recent progress of the joint modeling methods in recommendations, which includes multi-task, multi-scenario, multi-modal, and multi-behavior modeling. This work will provide academic researchers and industry professionals with a thorough understanding and clear insight into these areas, sparking new ideas, fostering discussions, and driving technological advancements in the field of deep recommender systems. Our tutorial homepage is released online https://applied-machine-learning-lab.github.io/Joint-Modeling-in-Deep-Recommender-Systems-WWW2025/.
Institutions: City University of Hong Kong, Huawei Technologies (China)
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
OpenAlex ID: W4410637826