Traditional recommender systems suggest items by learning from user preferences, but ignore other stakeholders in the whole system.

ABSTRACT. Traditional recommender systems suggest items by learning from user preferences, but ignore other stakeholders in the whole sys-.

Traditional recommender systems suggest items by learning from user preferences, but ignore other stakeholders in the whole system.

Oct 15, 2019 · In this paper, we concentrate on the research of fairness-aware recommendations in the reciprocal RS and propose an approach to bridge the gap ...

A contextual-bandit approach for multifaceted reciprocal recommendations in online dating ... Matchmaking Under Fairness Constraints: A Speed Dating Case Study.

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Apr 24, 2023 · Research on fairness in recommender systems is still a developing area. In this survey, we first review the fundamental concepts and notions of fairness.

A table of publications on fairness in recommender systems. This page will be periodically updated to include the most recent works.

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In this paper, we dive into the research of fairness-aware rec- ommendations in the reciprocal RS and propose an algorithm to rerank the recommendation list ...