# Learning to Recommend Using Non-Uniform Data

@article{Chen2021LearningTR, title={Learning to Recommend Using Non-Uniform Data}, author={Wan-Ping Chen and Mohsen Bayati}, journal={ArXiv}, year={2021}, volume={abs/2110.11248} }

Learning user preferences for products based on their past purchases or reviews is at the cornerstone of modern recommendation engines. One complication in this learning task is that some users are more likely to purchase products or review them, and some products are more likely to be purchased or reviewed by the users. This non-uniform pattern degrades the power of many existing recommendation algorithms, as they assume that the observed data is sampled uniformly at random among user-product…

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