Combating Heterogeneous Model Biases in Recommendations via Boosting
Year: 2025
Type: conference-paper
Abstract: Collaborative Filtering (CF) based recommenders often exhibit model biases, delivering strong recommendation utility to certain users or items at the expense of others. Prior research approaches these biases as isolated and standalone issues, ignoring their interconnected nature and developing separate methods, thereby compromising the specialized debiasing efforts. Thus, we introduce a boosting-based framework designed to alleviate a broad spectrum of biases. This framework employs a series of sub-models, each tailored for different user and item subgroups. Theoretically, our model ensures an exponentially decreasing upper bound on the training loss across all user and item types with increasing boosting iterations. Extensive experiments demonstrate its superior debiasing capabilities against state-of-the-art methods across four model bias types. Appendix, data and code are available at https://github.com/JP-25/CFBoost
Authors: Jinhao Pan, James Caverlee, Ziwei Zhu
Institutions: George Mason University, Texas A&M University
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
OpenAlex ID: W4407953144