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Leave No User Behind: Towards Improving the Utility of Recommender Systems fo...
This dataset is used for the experiment of improving the utility of recommender systems for non-mainstream users. -
Mitigating Mainstream Bias in Recommendation via Cost-sensitive Learning
This dataset is used for the experiment of mitigating mainstream bias in recommender systems. -
Mobile Taobao
The dataset is used for click-through rate (CTR) prediction task in recommender systems. -
Amazon Electronics
The dataset is used for click-through rate (CTR) prediction task in recommender systems. -
MovieLens 20M
The dataset used in this paper is the MovieLens 20M dataset, which contains ratings from 92,032 users on 20,000 movies.
