Controlling Popularity Bias in Learning-to-Rank Recommendation

@inproceedings{Abdollahpouri2017ControllingPB,
  title={Controlling Popularity Bias in Learning-to-Rank Recommendation},
  author={Himan Abdollahpouri and Robin D. Burke and Bamshad Mobasher},
  booktitle={RecSys},
  year={2017}
}
Many recommendation algorithms suffer from popularity bias in their output: popular items are recommended frequently and less popular ones rarely, if at all. However, less popular, long-tail items are precisely those that are often desirable recommendations. In this paper, we introduce a flexible regularization-based framework to enhance the long-tail coverage of recommendation lists in a learning-to-rank algorithm. We show that regularization provides a tunable mechanism for controlling the… CONTINUE READING

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