Deep Learning for News Recommender Systems: Designing neural architectures to tackle the challenges of news recommendation - Couverture souple
Synopsis
Recommender Systems (RS) have been popular in assisting users with their choices, thus enhancing their engagement with online services. News RS are aimed to personalize users experiences and help them discover relevant articles from a large and dynamic search space. Therefore, it is a challenging scenario for recommendations. Large publishers release hundreds of news daily, implying that they must deal with fast-growing numbers of items that get quickly outdated. News readers exhibit more unstable consumption behavior than users in other domains. External events, like breaking news, affect readers interests. In addition, the news domain experiences extreme levels of sparsity, as most users are anonymous.In this book, we provide a comprehensive introduction about Deep Learning architectures for RS and an effective neural meta-architecture is proposed: the CHAMELEON. Experiments performed with two large datasets have shown the effectiveness of the CHAMELEON for news recommendation on many quality factors such as accuracy, item coverage, novelty, and reduced item cold-start problem, when compared to other traditional and state-of-the-art session-based recommendation algorithms.
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Deep Learning for News Recommender Systems: Designing neural architectures to tackle the challenges of news recommendation
Edité par
LAP LAMBERT Academic Publishing, 2020
ISBN 10 : 6202552212
ISBN 13 : 9786202552219
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paperback. Etat : Good. Connecting readers with great books since 1972! Used textbooks may not include companion materials such as access codes, etc. May have some wear or writing/highlighting. We ship orders daily and Customer Service is our top priority! N° de réf. du vendeur S_402420053
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Deep Learning for News Recommender Systems
Edité par
LAP LAMBERT Academic Publishing Mai 2020, 2020
ISBN 10 : 6202552212
ISBN 13 : 9786202552219
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Vendeur : BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Allemagne
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Taschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Recommender Systems (RS) have been popular in assisting users with their choices, thus enhancing their engagement with online services. News RS are aimed to personalize users experiences and help them discover relevant articles from a large and dynamic search space. Therefore, it is a challenging scenario for recommendations. Large publishers release hundreds of news daily, implying that they must deal with fast-growing numbers of items that get quickly outdated. News readers exhibit more unstable consumption behavior than users in other domains. External events, like breaking news, affect readers interests. In addition, the news domain experiences extreme levels of sparsity, as most users are anonymous.In this book, we provide a comprehensive introduction about Deep Learning architectures for RS and an effective neural meta-architecture is proposed: the CHAMELEON. Experiments performed with two large datasets have shown the effectiveness of the CHAMELEON for news recommendation on many quality factors such as accuracy, item coverage, novelty, and reduced item cold-start problem, when compared to other traditional and state-of-the-art session-based recommendation algorithms. 188 pp. Englisch. N° de réf. du vendeur 9786202552219
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Deep Learning for News Recommender Systems: Designing neural architectures to tackle the challenges of news recommendation
Edité par
LAP LAMBERT Academic Publishing, 2020
ISBN 10 : 6202552212
ISBN 13 : 9786202552219
Neuf
Couverture souple
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Deep Learning for News Recommender Systems: Designing neural architectures to tackle the challenges of news recommendation
Edité par
LAP LAMBERT Academic Publishing, 2020
ISBN 10 : 6202552212
ISBN 13 : 9786202552219
Neuf
Couverture souple
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Deep Learning for News Recommender Systems: Designing neural architectures to tackle the challenges of news recommendation
Edité par
LAP LAMBERT Academic Publishing, 2020
ISBN 10 : 6202552212
ISBN 13 : 9786202552219
Neuf
Couverture souple
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Deep Learning for News Recommender Systems
Edité par
LAP LAMBERT Academic Publishing, 2020
ISBN 10 : 6202552212
ISBN 13 : 9786202552219
Neuf
Couverture souple
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Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Moreira GabrielGabriel Moreira obtained his DSc. degree at ITA (Brazil), researching about Deep Recommender Systems. Was recognized as a Google Developer Expert (GDE) for Machine Learning, being a featured speaker in conferences and . N° de réf. du vendeur 385947033
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Deep Learning for News Recommender Systems
Edité par
LAP LAMBERT Academic Publishing Mai 2020, 2020
ISBN 10 : 6202552212
ISBN 13 : 9786202552219
Neuf
Taschenbuch
Vendeur : buchversandmimpf2000, Emtmannsberg, BAYE, Allemagne
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Taschenbuch. Etat : Neu. This item is printed on demand - Print on Demand Titel. Neuware -Recommender Systems (RS) have been popular in assisting users with their choices, thus enhancing their engagement with online services. News RS are aimed to personalize users experiences and help them discover relevant articles from a large and dynamic search space. Therefore, it is a challenging scenario for recommendations. Large publishers release hundreds of news daily, implying that they must deal with fast-growing numbers of items that get quickly outdated. News readers exhibit more unstable consumption behavior than users in other domains. External events, like breaking news, affect readers interests. In addition, the news domain experiences extreme levels of sparsity, as most users are anonymous.In this book, we provide a comprehensive introduction about Deep Learning architectures for RS and an effective neural meta-architecture is proposed: the CHAMELEON. Experiments performed with two large datasets have shown the effectiveness of the CHAMELEON for news recommendation on many quality factors such as accuracy, item coverage, novelty, and reduced item cold-start problem, when compared to other traditional and state-of-the-art session-based recommendation algorithms.Books on Demand GmbH, Überseering 33, 22297 Hamburg 188 pp. Englisch. N° de réf. du vendeur 9786202552219
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Deep Learning for News Recommender Systems : Designing neural architectures to tackle the challenges of news recommendation
Edité par
LAP LAMBERT Academic Publishing, 2020
ISBN 10 : 6202552212
ISBN 13 : 9786202552219
Neuf
Taschenbuch
Vendeur : AHA-BUCH GmbH, Einbeck, Allemagne
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Taschenbuch. Etat : Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Recommender Systems (RS) have been popular in assisting users with their choices, thus enhancing their engagement with online services. News RS are aimed to personalize users experiences and help them discover relevant articles from a large and dynamic search space. Therefore, it is a challenging scenario for recommendations. Large publishers release hundreds of news daily, implying that they must deal with fast-growing numbers of items that get quickly outdated. News readers exhibit more unstable consumption behavior than users in other domains. External events, like breaking news, affect readers interests. In addition, the news domain experiences extreme levels of sparsity, as most users are anonymous.In this book, we provide a comprehensive introduction about Deep Learning architectures for RS and an effective neural meta-architecture is proposed: the CHAMELEON. Experiments performed with two large datasets have shown the effectiveness of the CHAMELEON for news recommendation on many quality factors such as accuracy, item coverage, novelty, and reduced item cold-start problem, when compared to other traditional and state-of-the-art session-based recommendation algorithms. N° de réf. du vendeur 9786202552219
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Deep Learning for News Recommender Systems | Designing neural architectures to tackle the challenges of news recommendation
Edité par
LAP LAMBERT Academic Publishing, 2020
ISBN 10 : 6202552212
ISBN 13 : 9786202552219
Neuf
Taschenbuch
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Taschenbuch. Etat : Neu. Deep Learning for News Recommender Systems | Designing neural architectures to tackle the challenges of news recommendation | Gabriel Moreira (u. a.) | Taschenbuch | 188 S. | Englisch | 2020 | LAP LAMBERT Academic Publishing | EAN 9786202552219 | Verantwortliche Person für die EU: BoD - Books on Demand, In de Tarpen 42, 22848 Norderstedt, info[at]bod[dot]de | Anbieter: preigu Print on Demand. N° de réf. du vendeur 118437126
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