recommendation systems based collaborative de benbrahim mohammed (4 resultados)

Taschenbuch. Condición: Neu. Recommendation Systems based on Collaborative Filtering | New approaches | Mohammed Benbrahim (u. a.) | Taschenbuch | Englisch | 2024 | Our Knowledge Publishing | EAN 9786207429196 | Verantwortliche Person für die EU: SIA OmniScriptum Publishing, Brivibas Gatve 197, 1039 RIGA, LETTLAND, customerservice[at]vdm-vsg[dot]de | Anbieter: preigu.

Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware 100 pp. Englisch.

Taschenbuch. Condición: 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.

Taschenbuch. Condición: 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.