Papers Exposure Fairness
“Exposure Fairness” 태그가 달린 논문 15편 · 필터 해제
Joint Evaluation of Fairness and Relevance in Recommender Systems with Pareto Frontier
Fairness and relevance are two important aspects of recommender systems (RSs). Typically, they are evaluated either (i) separately by individual measures of fairness and relevance, or (ii) jointly using a single measure …
Exposure FairnessFairnessRecommendation SystemsRe-RankingMitigating Exposure Bias in Online Learning to Rank Recommendation: A Novel Reward Model for Cascading Bandits
Exposure bias is a well-known issue in recommender systems where items and suppliers are not equally represented in the recommendation results. This bias becomes particularly problematic over time as a few items are repe…
Exposure FairnessFairnessLearning-To-RankMulti-Armed Bandits+1Can We Trust Recommender System Fairness Evaluation? The Role of Fairness and Relevance
Relevance and fairness are two major objectives of recommender systems (RSs). Recent work proposes measures of RS fairness that are either independent from relevance (fairness-only) or conditioned on relevance (joint mea…
Exposure FairnessFairnessRecommendation SystemsScalable and Provably Fair Exposure Control for Large-Scale Recommender Systems
Typical recommendation and ranking methods aim to optimize the satisfaction of users, but they are often oblivious to their impact on the items (e.g., products, jobs, news, video) and their providers. However, there has …
Collaborative FilteringExposure FairnessFairnessRecommendation SystemsSimultaneously Achieving Group Exposure Fairness and Within-Group Meritocracy in Stochastic Bandits
Existing approaches to fairness in stochastic multi-armed bandits (MAB) primarily focus on exposure guarantee to individual arms. When arms are naturally grouped by certain attribute(s), we propose Bi-Level Fairness, whi…
AttributeExposure FairnessFairnessMulti-Armed BanditsEvaluation Measures of Individual Item Fairness for Recommender Systems: A Critical Study
Fairness is an emerging and challenging topic in recommender systems. In recent years, various ways of evaluating and therefore improving fairness have emerged. In this study, we examine existing evaluation measures of f…
Exposure FairnessFairnessRecommendation SystemsFairness of Exposure in Dynamic Recommendation
Exposure bias is a well-known issue in recommender systems where the exposure is not fairly distributed among items in the recommendation results. This is especially problematic when bias is amplified over time as a few …
Exposure FairnessFairnessRecommendation SystemsFARA: Future-aware Ranking Algorithm for Fairness Optimization
Ranking systems are the key components of modern Information Retrieval (IR) applications, such as search engines and recommender systems. Besides the ranking relevance to users, the exposure fairness to item providers ha…
Exposure FairnessFairnessInformation RetrievalRecommendation SystemsJoint Multisided Exposure Fairness for Recommendation
Prior research on exposure fairness in the context of recommender systems has focused mostly on disparities in the exposure of individual or groups of items to individual users of the system. The problem of how individua…
Exposure FairnessFairnessInformation RetrievalRecommendation Systems+1FairGAN: GANs-based Fairness-aware Learning for Recommendations with Implicit Feedback
Ranking algorithms in recommender systems influence people to make decisions. Conventional ranking algorithms based on implicit feedback data aim to maximize the utility to users by capturing users’ preferences over item…
Exposure FairnessFairnessRecommendation SystemsVertical Allocation-based Fair Exposure Amortizing in Ranking
Result ranking often affects consumer satisfaction as well as the amount of exposure each item receives in the ranking services. Myopically maximizing customer satisfaction by ranking items only according to relevance wi…
Exposure FairnessFairnessRecommendation SystemsUnderstanding and Mitigating Multi-Sided Exposure Bias in Recommender Systems
Fairness is a critical system-level objective in recommender systems that has been the subject of extensive recent research. It is especially important in multi-sided recommendation platforms where it may be crucial to o…
Exposure FairnessFairnessRecommendation SystemsUnbiased Cascade Bandits: Mitigating Exposure Bias in Online Learning to Rank Recommendation
Exposure bias is a well-known issue in recommender systems where items and suppliers are not equally represented in the recommendation results. This is especially problematic when bias is amplified over time as a few pop…
Exposure FairnessFairnessLearning-To-RankRecommendation SystemsA Graph-based Approach for Mitigating Multi-sided Exposure Bias in Recommender Systems
Fairness is a critical system-level objective in recommender systems that has been the subject of extensive recent research. A specific form of fairness is supplier exposure fairness where the objective is to ensure equi…
DiversityExposure FairnessFairnessRecommendation SystemsUser-item matching for recommendation fairness
As we all know, users and item-providers are two main parties of participants in recommender systems. However, most existing research efforts on recommendation were focused on better serving users and overlooked the purp…
Exposure FairnessFairnessRecommendation Systems