Year: 2025
Type: conference-paper
Abstract: In sequential recommendation, system exposure refers to items that are exposed to the user. Typically, the user only interactions with a few of the exposed items. Although sequential recommendation has achieved great success in predicting future user interests, existing sequential recommendation methods do not fully exploit system exposure data. Most methods only model items that have been interacted with, while the large volume of exposed but non-interacted items is overlooked. Even methods that consider system exposure typically train the recommender using only the logged historical system exposure, without exploring unseen user interests.
Authors: Ziqi Zhao, Zhaochun Ren, Jiyuan Yang, Zuming Yan, Zihan Wang, Liu Yang, Pengjie Ren, Zhumin Chen, Maarten de Rijke, Xin Xin
Institutions: Shandong University, Leiden University, University of Amsterdam
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Awards: Fairness and Intersectional Non-Discrimination in Human Recommendation, G2862515139, G309477424, G5489207280, Hybrid Intelligence (HI): augmenting human intellect, G6216555950, G6283261777, G6493830415, Trustworthy AI-based Systems for Sustainable Growth, G6854351342, G708070633, G8283770779, G83507391
OpenAlex ID: W4412377812