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• W3172591566 abstract "Link prediction methods are frequently applied in recommender systems, e.g., to suggest citations for academic papers or friends in social networks. However, exposure bias can arise when users are systematically underexposed to certain relevant items. For example, in citation networks, authors might be more likely to encounter papers from their own field and thus cite them preferentially. This bias can propagate through naively trained link predictors, leading to both biased evaluation and high generalization error (as assessed by true relevance). Moreover, this bias can be exacerbated by feedback loops. We propose estimators that leverage known exposure probabilities to mitigate this bias and consequent feedback loops. Next, we provide a loss function for learning the exposure probabilities from data. Finally, experiments on semi-synthetic data based on real-world citation networks, show that our methods reliably identify (truly) relevant citations. Additionally, our methods lead to greater diversity in the recommended papers' fields of study. The code is available at this https URL." @default.
• W3172591566 created "2021-06-22" @default.
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• W3172591566 date "2021-06-13" @default.
• W3172591566 modified "2023-10-16" @default.
• W3172591566 title "Correcting Exposure Bias for Link Recommendation" @default.
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• W3172591566 hasPublicationYear "2021" @default.
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