Papers › An adaptive denoising recommendation algorithm for causal separation bias
An adaptive denoising recommendation algorithm for causal separation bias
Zhangqiuling, Xuhuayang, Wangjianfang
In recommender systems, user selection bias often influences user-item interactions, e.g., users are more likely to rate their previously preferred or popular items. Existing methods can leverage the impact of selection bias in user ratings on the evaluation and optimization of recommendation system. However, these methods either inevitably contain a large amount of noise in the sampling process or suffer from the confound between users’ conformity and interests. Inspired by the recent success of causal inference, in this work we propose a novel method to separate popularity biases for recommendation, named adaptive denoising and causal inference algorithm (ADA). We first compute the average rating of all feedback items of each user as the basis in converting explicit feedback to implicit feedback, and then obtain the true positive implicit data through adaptive denoising method. In addition, we separate the confounding of users’ conformity and interest in the selection bias by causal inference. Specifically, we construct a multi-task learning model with regularization loss functions. Experimental results on the two datasets demonstrate the superiority of our ADA model over state-of-the-art methods in recommendation accuracy.
Code
Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Tasks
Results from the paper archive 2025-07-28
No leaderboard rows for this paper in the archive.
Methods
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections