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- W4288804541 abstract "This paper studies user attributes in light of current concerns in the recommender system community: diversity, coverage, calibration, and data minimization. In experiments with a conventional context-aware recommender system that leverages side information, we show that user attributes do not always improve recommendation. Then, we demonstrate that user attributes can negatively impact diversity and coverage. Finally, we investigate the amount of information about users that ``survives'' from the training data into the recommendation lists produced by the recommender. This information is a weak signal that could in the future be exploited for calibration or studied further as a privacy leak." @default.
- W4288804541 created "2022-07-30" @default.
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- W4288804541 date "2022-07-28" @default.
- W4288804541 modified "2023-09-25" @default.
- W4288804541 title "Gender In Gender Out: A Closer Look at User Attributes in Context-Aware Recommendation" @default.
- W4288804541 doi "https://doi.org/10.48550/arxiv.2207.14218" @default.
- W4288804541 hasPublicationYear "2022" @default.
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