9783032241443 - addressing bias in information retrieval di mishra, harshit; soundarajan, sucheta (19 risultati)
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Paperback or Softback. Condizione: New. Addressing Bias in Information Retrieval. Book.
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Paperback. Condizione: 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.
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Taschenbuch. Condizione: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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 th…e 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.
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Taschenbuch. Condizione: Neu. Addressing Bias in Information Retrieval | Harshit Mishra (u. a.) | Taschenbuch | SpringerBriefs in Intelligent Systems | x | Englisch | 2026 | Springer | EAN 9783032241443 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springe…r[dot]com | Anbieter: preigu.
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Paperback. Condizione: 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.
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Paperback. Condizione: 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, th…e 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.
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Paperback. Condizione: Brand New. 89 pages. 6.14x0.19x9.21 inches. In Stock. This item is printed on demand.
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Taschenbuch. Condizione: 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.
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Paperback. Condizione: 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, th…e 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. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.
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Taschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. 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 exi…st 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.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 92 pp. Englisch.
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Paperback. Condizione: 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, th…e 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 our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.
