Results 1 to 10 of about 50,074 (76)
Analyzing Item Popularity Bias of Music Recommender Systems: Are Different Genders Equally Affected? [PDF]
, 2021 Several studies have identified discrepancies between the popularity of items in user profiles and the corresponding recommendation lists. Such behavior, which concerns a variety of recommendation algorithms, is referred to as popularity bias. Existing work predominantly adopts simple statistical measures, such as the difference of mean or median ...
arxiv +1 more source
Testing popularity in linear time via maximum matching [PDF]
Testing popularity in linear time via maximum matching, Discrete
Applied Mathematics, Volume 366, 2025, Pages 152-160, ISSN 0166-218X, 2023 Popularity is an approach in mechanism design to find fair structures in a graph, based on the votes of the nodes. Popular matchings are the relaxation of stable matchings: given a graph G=(V,E) with strict preferences on the neighbors of the nodes, a matching M is popular if there is no other matching M' such that the number of nodes preferring M' is ...
arxiv +1 more source
An Adaptive Boosting Technique to Mitigate Popularity Bias in Recommender System [PDF]
arXiv, 2021 The observed ratings in most recommender systems are subjected to popularity bias and are thus not randomly missing. Due to this, only a few popular items are recommended, and a vast number of non-popular items are hardly recommended. Not suggesting the non-popular items lead to fewer products dominating the market and thus offering fewer opportunities
arxiv
Popular Matchings with One-Sided Bias [PDF]
arXiv, 2022 Let $G = (A \cup B,E)$ be a bipartite graph where the set $A$ consists of agents or main players and the set $B$ consists of jobs or secondary players. Every vertex has a strict ranking of its neighbors. A matching $M$ is popular if for any matching $N$, the number of vertices that prefer $M$ to $N$ is at least the number that prefer $N$ to $M ...
arxiv
User-centered Evaluation of Popularity Bias in Recommender Systems [PDF]
, 2021 Recommendation and ranking systems are known to suffer from popularity bias; the tendency of the algorithm to favor a few popular items while under-representing the majority of other items. Prior research has examined various approaches for mitigating popularity bias and enhancing the recommendation of long-tail, less popular, items.
arxiv +1 more source
Potential Factors Leading to Popularity Unfairness in Recommender Systems: A User-Centered Analysis [PDF]
arXiv, 2023 Popularity bias is a well-known issue in recommender systems where few popular items are over-represented in the input data, while majority of other less popular items are under-represented. This disparate representation often leads to bias in exposure given to the items in the recommendation results.
arxiv
Your Tribe Decides Your Vibe: Analyzing Local Popularity in the US Patent Citation Network [PDF]
arXiv, 2021 In many networks, the indegree of a vertex is a measure of its popularity. Past research has studied indegree distributions treating the network as a whole. In the US Patent citation network (USPCN), patents are classified into categories and subcategories.
arxiv
Popularity Degradation Bias in Local Music Recommendation [PDF]
arXiv, 2023 In this paper, we study the effect of popularity degradation bias in the context of local music recommendations. Specifically, we examine how accurate two top-performing recommendation algorithms, Weight Relevance Matrix Factorization (WRMF) and Multinomial Variational Autoencoder (Mult-VAE), are at recommending artists as a function of artist ...
arxiv
Finding popular branchings in vertex-weighted digraphs [PDF]
arXiv, 2021 Popular matchings have been intensively studied recently as a relaxed concept of stable matchings. By applying the concept of popular matchings to branchings in directed graphs, Kavitha et al.\ (2020) introduced popular branchings. In a directed graph $G=(V_G,E_G)$, each vertex has preferences over its incoming edges. For branchings $B_1$ and $B_2$ in $
arxiv
The Unfairness of Popularity Bias in Book Recommendation [PDF]
arXiv, 2022 Recent studies have shown that recommendation systems commonly suffer from popularity bias. Popularity bias refers to the problem that popular items (i.e., frequently rated items) are recommended frequently while less popular items are recommended rarely or not at all. Researchers adopted two approaches to examining popularity bias: (i) from the users'
arxiv