Harshit Mishra (23 results)

Paperback. Condition: Brand New. 74 pages. 9.00x6.00x0.19 inches. In Stock.

Paperback or Softback. Condition: New. Addressing Bias in Information Retrieval. Book.

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.…

Condition: New.

Condition: New.

Paperback. Condition: Brand New. 89 pages. 6.14x0.19x9.21 inches. In Stock.

Condition: New.

Condition: NEW.

Condition: New.

Taschenbuch. Condition: 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]springer[dot]com | Anbieter: preigu. …

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.…

Condition: new. Questo è un articolo print on demand.

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.…

Paperback. Condition: Brand New. 89 pages. 6.14x0.19x9.21 inches. In Stock. This item is printed on demand.

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.…

Condition: New. Print on Demand.

Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - There are a number of problems in the prior art, those are topics of research inputs likes ranges of the drag force generated by the vehicle, lift force at high vehicle motion velocities for compensation of the vehicle weight, Aerodynamic aspects of operation of the vehicle, Stream wise stability of vehicle motion and levitation and breaking of the vehicles and supersonic speed is not achieved in any mode of transportation. But this technology related to high speed magnetic levitating transportation. More particularly, this technology is related to high speed magnetic levitating transportation using compressed air chamber in the transportation vehicle.This is more particularly related to high speed vehicle levitated on a vacuum tunnel by using electromagnetic levitation. As this vehicle will move from one place to another in a vacuum environment and this vehicle will levitate above track with the help of electromagnets.The important thing is its motion, which is possible due to reaction force of high pressure air, coming out from compressed air chamber present in vehicle. It can give us the acceleration as per load requirement and it can achieve supersonic speed in few seconds.…

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Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Mishra RajatRajat Mishra, Studied Bachelor of Technology at KIET Group of Institutions APJAKTU, The Republic of India.Published a research paper High Speed Vacuum Air Vehicle in a conference organized by International Maglev Board .…

Condition: New. Print on Demand.

Condition: New. PRINT ON DEMAND.

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. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…

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 our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.…