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344.086, VL Learning from User-generated Data, Markus Schedl, 2026S

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The idea behind the "Delta" metrics proposed in [Lesota et al., 2021] is to measure the discrepancy between popularity of items in the user's interaction history and the popularity of items in the recommendation list.

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Assume 25% of the songs in the world have been created by female artists. The recommendation lists proposed by a recommender, on average, contain 25% songs by female artists. Considering an ideal world where we would like to have a 50%:50% share between male and female artists (for the sake of simplicity, neglecting the existence of other genders), such a recommender system would be prone to societal bias.

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The goal of bias mitigation via adversarial learning is to make the recommendation algorithm/model blind to a protected attribute while preserving accuracy.

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Assume 25% of the songs in the world have been created by female artists. The recommendation lists proposed by a recommender, on average, contain 25% songs by female artists. Considering an ideal world where we would like to have a 50%:50% share between male and female artists (for the sake of simplicity, neglecting the existence of other genders), such a recommender system would be prone to statistical bias.

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Filtering items created by the majority group of producers (e.g., males) from the list of items a recommender produces is considered an in-processing bias mitigation strategy.

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A recommender system that provides perfectly calibrated recommendations in terms of the user's genre distribution of consumed items (e.g., movies or music) reduces the popularity bias to zero.

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The RecGap metric proposed in [Melchiorre et al., 2021] measures the calibration mismatch between items in the user's interaction history and in the list of recommendations.

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In a dimensional model of affect, the valence dimension describes the pleasantness of an emotion or mood. The arousal dimension describes the intensity of the affect.

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According to [Yang and Huang, 2019], the personality of an item (a game) for their personality-aware recommender systems can be extracted from reviews or inferred from social media posts of the users playing the game.

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The Geneva Emotion Wheel according to [Scherer, 2005] is an example of a dimensional model of human affect.

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