9786202552219 - Deep Learning for News Recommender Systems: Designing neural architectures to tackle the challenges of news recommendation de Moreira, Gabriel; Cunha, Adilson (6 résultats)

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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.

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release hundreds of news daily, implying that they must deal with fast-growing numbers of items that get quickly outdated.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.

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 .

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.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 188 pp. Englisch.

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.