Calibrated recommendations
Harald Steck
- 27 Sep 2018
pp 154-162
TL;DR: Metrics for quantifying the degree of calibration, as well as a simple yet effective re-ranking algorithm for post-processing the output of recommender systems, are outlined.
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Abstract: When a user has watched, say, 70 romance movies and 30 action movies, then it is reasonable to expect the personalized list of recommended movies to be comprised of about 70% romance and 30% action movies as well. This important property is known as calibration, and recently received renewed attention in the context of fairness in machine learning. In the recommended list of items, calibration ensures that the various (past) areas of interest of a user are reflected with their corresponding proportions. Calibration is especially important in light of the fact that recommender systems optimized toward accuracy (e.g., ranking metrics) in the usual offline-setting can easily lead to recommendations where the lesser interests of a user get crowded out by the user's main interests-which we show empirically as well as in thought-experiments. This can be prevented by calibrated recommendations. To this end, we outline metrics for quantifying the degree of calibration, as well as a simple yet effective re-ranking algorithm for post-processing the output of recommender systems.
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TL;DR: This survey reviews over 60 papers published in top conferences/journals and provides an elaborate taxonomy of fairness methods in the recommendation, and outlines some promising future directions on fairness in recommendation.
Causal Intervention for Leveraging Popularity Bias in Recommendation
TL;DR: Zhang et al. as mentioned in this paper propose a new training and inference paradigm for recommendation named Popularity-bias Deconfounding and Adjusting (PDA), which removes the confounding popularity bias in model training and adjusts the recommendation score with desired popularity bias via causal intervention.
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Wenjie Wang,Fuli Feng,Xiangnan He,Xiang Wang,Tat-Seng Chua +4 more
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