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Open Access
June 2024
Toward Bias-Agnostic Recommender Systems: A Universal Generative Framework
ACM Transactions on Information Systems (TOIS), Volume 42, Issue 6Article No.: 142, Pages 1–30https://doi.org/10.1145/3655617
User behavior data, such as ratings and clicks, has been widely used to build personalizing models for recommender systems. However, many unflattering factors (e.g., popularity, ranking position, users’ selection) significantly affect the performance of ...
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