International Workshop on Algorithmic Bias in Search and Recommendation最新文献

Do you MIND? Reflections on the MIND dataset for research on diversity in news recommendations 你介意吗?关于MIND数据集对新闻推荐多样性研究的思考

International Workshop on Algorithmic Bias in Search and Recommendation Pub Date : 2023-04-17 DOI: 10.48550/arXiv.2304.08253

Sanne Vrijenhoek

引用次数: 0

Addressing Biases in the Texts using an End-to-End Pipeline Approach 使用端到端管道方法解决文本中的偏差

International Workshop on Algorithmic Bias in Search and Recommendation Pub Date : 2023-03-13 DOI: 10.48550/arXiv.2303.07024

S. Raza, S. Bashir, Sneha, Urooj Qamar

引用次数: 0

A Study on Accuracy, Miscalibration, and Popularity Bias in Recommendations 推荐中准确性、误差校准和流行偏差的研究

International Workshop on Algorithmic Bias in Search and Recommendation Pub Date : 2023-03-01 DOI: 10.48550/arXiv.2303.00400

Dominik Kowald, Gregory Mayr, M. Schedl, E. Lex

{"title":"A Study on Accuracy, Miscalibration, and Popularity Bias in Recommendations","authors":"Dominik Kowald, Gregory Mayr, M. Schedl, E. Lex","doi":"10.48550/arXiv.2303.00400","DOIUrl":"https://doi.org/10.48550/arXiv.2303.00400","url":null,"abstract":"Recent research has suggested different metrics to measure the inconsistency of recommendation performance, including the accuracy difference between user groups, miscalibration, and popularity lift. However, a study that relates miscalibration and popularity lift to recommendation accuracy across different user groups is still missing. Additionally, it is unclear if particular genres contribute to the emergence of inconsistency in recommendation performance across user groups. In this paper, we present an analysis of these three aspects of five well-known recommendation algorithms for user groups that differ in their preference for popular content. Additionally, we study how different genres affect the inconsistency of recommendation performance, and how this is aligned with the popularity of the genres. Using data from LastFm, MovieLens, and MyAnimeList, we present two key findings. First, we find that users with little interest in popular content receive the worst recommendation accuracy, and that this is aligned with miscalibration and popularity lift. Second, our experiments show that particular genres contribute to a different extent to the inconsistency of recommendation performance, especially in terms of miscalibration in the case of the MyAnimeList dataset.","PeriodicalId":165601,"journal":{"name":"International Workshop on Algorithmic Bias in Search and Recommendation","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131564632","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

International Workshop on Algorithmic Bias in Search and Recommendation Pub Date : 2022-03-01 DOI: 10.48550/arXiv.2203.00376

Dominik Kowald, Emanuel Lacić

{"title":"Popularity Bias in Collaborative Filtering-Based Multimedia Recommender Systems","authors":"Dominik Kowald, Emanuel Lacić","doi":"10.48550/arXiv.2203.00376","DOIUrl":"https://doi.org/10.48550/arXiv.2203.00376","url":null,"abstract":"Multimedia recommender systems suggest media items, e.g., songs, (digital) books and movies, to users by utilizing concepts of traditional recommender systems such as collaborative filtering. In this paper, we investigate a potential issue of such collaborative-filtering based multimedia recommender systems, namely popularity bias that leads to the underrepresentation of unpopular items in the recommendation lists. Therefore, we study four multimedia datasets, i.e., LastFm, MovieLens, BookCrossing and MyAnimeList, that we each split into three user groups differing in their inclination to popularity, i.e., LowPop, MedPop and HighPop. Using these user groups, we evaluate four collaborative filtering-based algorithms with respect to popularity bias on the item and the user level. Our findings are three-fold: firstly, we show that users with little interest into popular items tend to have large user profiles and thus, are important data sources for multimedia recommender systems. Secondly, we find that popular items are recommended more frequently than unpopular ones. Thirdly, we find that users with little interest into popular items receive significantly worse recommendations than users with medium or high interest into popularity.","PeriodicalId":165601,"journal":{"name":"International Workshop on Algorithmic Bias in Search and Recommendation","volume":"264 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123260057","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 8

The Unfairness of Popularity Bias in Book Recommendation 图书推荐中流行偏见的不公平性

International Workshop on Algorithmic Bias in Search and Recommendation Pub Date : 2022-02-27 DOI: 10.1007/978-3-031-09316-6_7

Mohammadmehdi Naghiaei, Hossein A. Rahmani, M. Dehghan

引用次数: 15

The Unfairness of Active Users and Popularity Bias in Point-of-Interest Recommendation 兴趣点推荐中活跃用户的不公平性与流行度偏差

International Workshop on Algorithmic Bias in Search and Recommendation Pub Date : 2022-02-27 DOI: 10.1007/978-3-031-09316-6_6

Hossein A. Rahmani, Yashar Deldjoo, Ali Tourani, Mohammadmehdi Naghiaei

引用次数: 18

Incentives for Item Duplication Under Fair Ranking Policies 公平排名政策下项目重复的激励机制

International Workshop on Algorithmic Bias in Search and Recommendation Pub Date : 2021-10-29 DOI: 10.1007/978-3-030-78818-6_7

G. Nunzio, Alessandro Fabris, Gianmaria Silvello, Gian Antonio Susto

引用次数: 2

Detecting race and gender bias in visual representation of AI on web search engines 在网络搜索引擎上检测人工智能视觉表现中的种族和性别偏见

International Workshop on Algorithmic Bias in Search and Recommendation Pub Date : 2021-06-26 DOI: 10.1007/978-3-030-78818-6_5

M. Makhortykh, Aleksandra Urman, R. Ulloa

引用次数: 13

Users' Perception of Search Engine Biases and Satisfaction 用户对搜索引擎偏差的感知与满意度

International Workshop on Algorithmic Bias in Search and Recommendation Pub Date : 2021-04-01 DOI: 10.1007/978-3-030-78818-6_3

Bin Han, C. Shah, Daniel Saelid

引用次数: 4

Mitigating Gender Bias in Machine Learning Data Sets 减少机器学习数据集中的性别偏见

International Workshop on Algorithmic Bias in Search and Recommendation Pub Date : 2020-04-14 DOI: 10.1007/978-3-030-52485-2_2

Susan Leavy, G. Meaney, Karen Wade, Derek Greene

引用次数: 21