Items related to Addressing Bias in Information Retrieval (SpringerBriefs...
Synopsis
Online search engines are an essential tool for seeking information, but results returned from these search engines can contain undesirable forms of bias with respect to protected attributes such as gender or race. These biases can exist due to the word embeddings used by search engines, the design of re-ranking algorithms, the development of retrieval algorithms, or a variety of other reasons. Classical information retrieval (IR) methods, such as query recommendation or query expansion, were designed to produce the most relevant results. However, if such biases are present in the system, then these methods will also deliver biased results.
IR systems/recommender systems also play a major role in social media algorithms, where platforms have pivoted away from friend-follow timelines to “for you” timelines containing algorithmically-selected content. If these algorithms are biased (towards, say, maximizing screen time to show ads, maximizing user interaction to likes, comments), then they may push end users towards clickbait or non-mainstream trending topics.
This book presents an overview of modern IR and discusses the work done to mitigate biases in IR systems. It also examines methods for debiasing word embeddings and re-ranking search results to address group fairness, and presents a query reformulation method that analyzes bias in search results and delivers balanced results to the end user.
Awareness of how information retrieval systems work, ways to mitigate bias in search results, and the tradeoffs between accuracy and bias metrics in search results will help readers understand real-world search engines.
"synopsis" may belong to another edition of this title.
About the Author
Harshit Mishra is a Ph.D. student in the Department of Electrical Engineering and Computer Science at Syracuse University. He holds a Master of Science degree in Computer Science from Syracuse University. His research interests include natural language processing, algorithmic fairness, network science, and AI for social good.
Sucheta Soundarajan is an Associate Professor in the Department of Electrical Engineering and Computer Science at Syracuse University. She received her Ph.D. in Computer Science from Cornell University. Her research interests include the theory and applications of network science, algorithmic fairness, and AI in government.
"About this title" may belong to another edition of this title.
Search results for Addressing Bias in Information Retrieval (SpringerBriefs...
Stock Image
Addressing Bias in Information Retrieval (eng)
Print on DemandSeller: Brook Bookstore On Demand, Napoli, NA, Italy
Seller rating 5 out of 5 stars
Condition: new. Questo è un articolo print on demand. Seller Inventory # BVDQVEDP09
Buy New
£ 40.88
£ 3.43 shipping
Ships from Italy to U.S.A.
Ships from Italy to U.S.A.
Quantity: Over 20 available
Seller Image
Addressing Bias in Information Retrieval (Paperback or Softback)
Published by
Springer 5/23/2026, 2026
ISBN 10: 3032241448
ISBN 13: 9783032241443
New
Paperback or Softback
Seller: BargainBookStores, Grand Rapids, MI, U.S.A.
Seller rating 5 out of 5 stars
Paperback or Softback. Condition: New. Addressing Bias in Information Retrieval. Book. Seller Inventory # BBS-9783032241443
Seller Image
Addressing Bias in Information Retrieval
Published by
Springer Nature Switzerland AG, CH, 2026
ISBN 10: 3032241448
ISBN 13: 9783032241443
New
Paperback
Seller: Rarewaves.com USA, London, LONDO, United Kingdom
Seller rating 5 out of 5 stars
Paperback. Condition: New. Online search engines are an essential tool for seeking information, but results returned from these search engines can contain undesirable forms of bias with respect to protected attributes such as gender or race. These biases can exist due to the word embeddings used by search engines, the design of re-ranking algorithms, the development of retrieval algorithms, or a variety of other reasons. Classical information retrieval (IR) methods, such as query recommendation or query expansion, were designed to produce the most relevant results. However, if such biases are present in the system, then these methods will also deliver biased results. IR systems/recommender systems also play a major role in social media algorithms, where platforms have pivoted away from friend-follow timelines to "for you" timelines containing algorithmically-selected content. If these algorithms are biased (towards, say, maximizing screen time to show ads, maximizing user interaction to likes, comments), then they may push end users towards clickbait or non-mainstream trending topics. This book presents an overview of modern IR and discusses the work done to mitigate biases in IR systems. It also examines methods for debiasing word embeddings and re-ranking search results to address group fairness, and presents a query reformulation method that analyzes bias in search results and delivers balanced results to the end user.Awareness of how information retrieval systems work, ways to mitigate bias in search results, and the tradeoffs between accuracy and bias metrics in search results will help readers understand real-world search engines. Seller Inventory # LU-9783032241443
Buy New
£ 51.77
Free Shipping
Ships from United Kingdom to U.S.A.
Ships from United Kingdom to U.S.A.
Quantity: 1 available
Stock Image
Addressing Bias in Information Retrieval (SpringerBriefs in Intelligent Systems)
Seller: California Books, Miami, FL, U.S.A.
Seller rating 4 out of 5 stars
Condition: New. Seller Inventory # I-9783032241443
Stock Image
Addressing Bias in Information Retrieval (Paperback)
Published by
Springer Nature Switzerland AG, Cham, 2026
ISBN 10: 3032241448
ISBN 13: 9783032241443
New
Paperback
Seller: Grand Eagle Retail, Bensenville, IL, U.S.A.
Seller rating 5 out of 5 stars
Paperback. Condition: new. Paperback. Online search engines are an essential tool for seeking information, but results returned from these search engines can contain undesirable forms of bias with respect to protected attributes such as gender or race. These biases can exist due to the word embeddings used by search engines, the design of re-ranking algorithms, the development of retrieval algorithms, or a variety of other reasons. Classical information retrieval (IR) methods, such as query recommendation or query expansion, were designed to produce the most relevant results. However, if such biases are present in the system, then these methods will also deliver biased results. IR systems/recommender systems also play a major role in social media algorithms, where platforms have pivoted away from friend-follow timelines to for you timelines containing algorithmically-selected content. If these algorithms are biased (towards, say, maximizing screen time to show ads, maximizing user interaction to likes, comments), then they may push end users towards clickbait or non-mainstream trending topics. This book presents an overview of modern IR and discusses the work done to mitigate biases in IR systems. It also examines methods for debiasing word embeddings and re-ranking search results to address group fairness, and presents a query reformulation method that analyzes bias in search results and delivers balanced results to the end user.Awareness of how information retrieval systems work, ways to mitigate bias in search results, and the tradeoffs between accuracy and bias metrics in search results will help readers understand real-world search engines. It also examines methods for debiasing word embeddings and re-ranking search results to address group fairness, and presents a query reformulation method that analyzes bias in search results and delivers balanced results to the end user. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Seller Inventory # 9783032241443
Stock Image
Addressing Bias in Information Retrieval (SpringerBriefs in Intelligent Systems)
Print on DemandSeller: Revaluation Books, Exeter, United Kingdom
Seller rating 5 out of 5 stars
Paperback. Condition: Brand New. 89 pages. 6.14x0.19x9.21 inches. In Stock. This item is printed on demand. Seller Inventory # __3032241448
Buy New
£ 50.61
£ 10 shipping
Ships from United Kingdom to U.S.A.
Ships from United Kingdom to U.S.A.
Quantity: 1 available
Seller Image
Addressing Bias in Information Retrieval
Published by
Springer, Berlin, Springer Jul 2026, 2026
ISBN 10: 3032241448
ISBN 13: 9783032241443
New
Taschenbuch
Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germany
Seller rating 5 out of 5 stars
Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Online search engines are an essential tool for seeking information, but results returned from these search engines can contain undesirable forms of bias with respect to protected attributes such as gender or race. These biases can exist due to the word embeddings used by search engines, the design of re-ranking algorithms, the development of retrieval algorithms, or a variety of other reasons. Classical information retrieval (IR) methods, such as query recommendation or query expansion, were designed to produce the most relevant results. However, if such biases are present in the system, then these methods will also deliver biased results.IR systems/recommender systems also play a major role in social media algorithms, where platforms have pivoted away from friend-follow timelines to for you timelines containing algorithmically-selected content.If these algorithms are biased (towards, say, maximizing screen time to show ads, maximizing user interaction to likes, comments), then they may push end users towards clickbait or non-mainstream trending topics.This book presents an overview of modern IR and discusses the work done to mitigate biases in IR systems. It also examines methods for debiasing word embeddings and re-ranking search results to address group fairness, and presents a query reformulation method that analyzes bias in search results and delivers balanced results to the end user.Awareness of how information retrieval systems work, ways to mitigate bias in search results, and the tradeoffs between accuracy and bias metrics in search results will help readers understand real-world search engines. 79 pp. Englisch. Seller Inventory # 9783032241443
Stock Image
Addressing Bias in Information Retrieval (SpringerBriefs in Intelligent Systems)
Seller: Revaluation Books, Exeter, United Kingdom
Seller rating 5 out of 5 stars
Paperback. Condition: Brand New. 89 pages. 6.14x0.19x9.21 inches. In Stock. Seller Inventory # x-3032241448
Buy New
£ 62.76
£ 10 shipping
Ships from United Kingdom to U.S.A.
Ships from United Kingdom to U.S.A.
Quantity: 2 available
Stock Image
Addressing Bias in Information Retrieval (SpringerBriefs in Intelligent Systems)
Seller: Books Puddle, New York, NY, U.S.A.
Seller rating 4 out of 5 stars
Condition: New. Seller Inventory # 26405742995
Stock Image
ADDRESSING BIAS IN INFORMATION RETRIEVAL
Seller: Speedyhen, Hertfordshire, United Kingdom
Seller rating 5 out of 5 stars
Condition: NEW. Seller Inventory # NW9783032241443
Buy New
£ 37.71
£ 41 shipping
Ships from United Kingdom to U.S.A.
Ships from United Kingdom to U.S.A.
Quantity: 1 available