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Deep Learning for News Recommender Systems: Designing neural architectures to tackle the challenges of news recommendation - Softcover
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release hundreds of news daily, implying that they must deal with fast-growing numbers of items that get quickly outdated.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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- PublisherLAP LAMBERT Academic Publishing
- Publication date2020
- ISBN 10 6202552212
- ISBN 13 9786202552219
- BindingPaperback
- LanguageEnglish
- Number of pages188
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Deep Learning for News Recommender Systems: Designing neural architectures to tackle the challenges of news recommendation
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LAP LAMBERT Academic Publishing, 2020
ISBN 10: 6202552212
ISBN 13: 9786202552219
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Deep Learning for News Recommender Systems: Designing neural architectures to tackle the challenges of news recommendation
Published by
LAP LAMBERT Academic Publishing, 2020
ISBN 10: 6202552212
ISBN 13: 9786202552219
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Softcover
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Deep Learning for News Recommender Systems
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LAP LAMBERT Academic Publishing Mai 2020, 2020
ISBN 10: 6202552212
ISBN 13: 9786202552219
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Taschenbuch. Condition: 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. Seller Inventory # 9786202552219
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Deep Learning for News Recommender Systems: Designing neural architectures to tackle the challenges of news recommendation
Published by
LAP LAMBERT Academic Publishing, 2020
ISBN 10: 6202552212
ISBN 13: 9786202552219
New
Softcover
Seller: Majestic Books, Hounslow, United Kingdom
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Deep Learning for News Recommender Systems : Designing neural architectures to tackle the challenges of news recommendation
Published by
LAP LAMBERT Academic Publishing, 2020
ISBN 10: 6202552212
ISBN 13: 9786202552219
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Taschenbuch. Condition: 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. Seller Inventory # 9786202552219
Quantity: 1 available
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Deep Learning for News Recommender Systems: Designing neural architectures to tackle the challenges of news recommendation
Published by
LAP LAMBERT Academic Publishing, 2020
ISBN 10: 6202552212
ISBN 13: 9786202552219
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Deep Learning for News Recommender Systems
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ISBN 10: 6202552212
ISBN 13: 9786202552219
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Condition: 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 . Seller Inventory # 385947033
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Deep Learning for News Recommender Systems
Published by
LAP LAMBERT Academic Publishing Mai 2020, 2020
ISBN 10: 6202552212
ISBN 13: 9786202552219
New
Taschenbuch
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Taschenbuch. Condition: Neu. 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. Seller Inventory # 9786202552219
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