Inhaltsangabe
Collaborative filtering (CF) is a popular recommendation approach that has been extensively researched over the last two decades, resulting in a diverse set of algorithms and a large collection of tools to evaluate their performance. This research proposes a new recommendation approach to deal with the problems of grey sheep and data sparsity, with the aim of improving prediction accuracy by inferring new users from existing users in datasets. This transformation creates users with preferences opposite to those of real users, thereby increasing the number of users and solving the two problems mentioned. The performance of this approach has been evaluated using two datasets, MovieLens and FilmTrust. Overall, this book contributes to the development of better recommender systems capable of overcoming the challenges of data overload and improving user experience.
Die Inhaltsangabe kann sich auf eine andere Ausgabe dieses Titels beziehen.
Über die Autorin bzw. den Autor
Abdellah El Fazziki ist Doktor der Informatik an der Universität Sidi Mohamed Ben Abdellah und hat sich auf Empfehlungssysteme spezialisiert.Mohammed Benbrahim ist Professor an der Universität Sidi Mohamed Ben Abdellah, Koordinator des Masterstudiengangs Smart Industry und Direktor des Labors für Systemtechnik, -modellierung und -analyse.
„Über diesen Titel“ kann sich auf eine andere Ausgabe dieses Titels beziehen.
Suchergebnisse für Recommendation Systems based on Collaborative Filtering:...
Beispielbild für diese ISBN
Recommendation Systems based on Collaborative Filtering
Verlag:
Our Knowledge Publishing Apr 2024, 2024
ISBN 10: 6207429192
ISBN 13: 9786207429196
Neu
Taschenbuch
Anbieter: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Deutschland
Verkäuferbewertung 5 von 5 Sternen
Taschenbuch. Zustand: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware 100 pp. Englisch. Bestandsnummer des Verkäufers 9786207429196
Neu kaufen
EUR 60,90
EUR 23,00 Versand
Versand von Deutschland nach USA
Versand von Deutschland nach USA
Anzahl: 2 verfügbar
Foto des Verkäufers
Recommendation Systems based on Collaborative Filtering
Verlag:
Our Knowledge Publishing Apr 2024, 2024
ISBN 10: 6207429192
ISBN 13: 9786207429196
Neu
Taschenbuch
Anbieter: buchversandmimpf2000, Emtmannsberg, BAYE, Deutschland
Verkäuferbewertung 5 von 5 Sternen
Taschenbuch. Zustand: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Collaborative filtering (CF) is a popular recommendation approach that has been extensively researched over the last two decades, resulting in a diverse set of algorithms and a large collection of tools to evaluate their performance. This research proposes a new recommendation approach to deal with the problems of grey sheep and data sparsity, with the aim of improving prediction accuracy by inferring new users from existing users in datasets. This transformation creates users with preferences opposite to those of real users, thereby increasing the number of users and solving the two problems mentioned. The performance of this approach has been evaluated using two datasets, MovieLens and FilmTrust. Overall, this book contributes to the development of better recommender systems capable of overcoming the challenges of data overload and improving user experience.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 100 pp. Englisch. Bestandsnummer des Verkäufers 9786207429196
Neu kaufen
EUR 60,90
EUR 60,00 Versand
Versand von Deutschland nach USA
Versand von Deutschland nach USA
Anzahl: 1 verfügbar
Foto des Verkäufers
Recommendation Systems based on Collaborative Filtering : New approaches
Print-on-DemandAnbieter: AHA-BUCH GmbH, Einbeck, Deutschland
Verkäuferbewertung 5 von 5 Sternen
Taschenbuch. Zustand: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Collaborative filtering (CF) is a popular recommendation approach that has been extensively researched over the last two decades, resulting in a diverse set of algorithms and a large collection of tools to evaluate their performance. This research proposes a new recommendation approach to deal with the problems of grey sheep and data sparsity, with the aim of improving prediction accuracy by inferring new users from existing users in datasets. This transformation creates users with preferences opposite to those of real users, thereby increasing the number of users and solving the two problems mentioned. The performance of this approach has been evaluated using two datasets, MovieLens and FilmTrust. Overall, this book contributes to the development of better recommender systems capable of overcoming the challenges of data overload and improving user experience. Bestandsnummer des Verkäufers 9786207429196
Neu kaufen
EUR 61,63
EUR 60,84 Versand
Versand von Deutschland nach USA
Versand von Deutschland nach USA
Anzahl: 1 verfügbar