web personalized recommender system de shinde subhash (5 resultados)

Taschenbuch. Condición: Neu. Neuware -This book is to propose an adaptive recommendation model with learning algorithms, which increases web user satisfaction and save on thecosts of content management with minimal human intervention. This researchwork explores a unified model for hybrid filtering with learning algorithms which extracts customer¿s current browsing patterns and forms group of customersusing different clustering algorithms to obtain implicit users rating forrecommended product. In this research following three novel recommender systems are proposed. These systems are used to investigate issues and challenges related to recommendersystems. ¿ Hybrid web personalized recommender system based on web usagemining (HWPRS). ¿ Hybrid web personalized recommender system using centeringbunchingbased clustering (CBBCHPRS). ¿ Hybrid Fuzzy personalized recommender system using Modified Fuzzyc-means clustering (MFCMHFRS).Books on Demand GmbH, Überseering 33, 22297 Hamburg 140 pp. Englisch.

Paperback. Condición: Brand New. 140 pages. 8.66x5.91x0.32 inches. In Stock.

Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book is to propose an adaptive recommendation model with learning algorithms, which increases web user satisfaction and save on thecosts of content management with minimal human intervention. This researchwork explores a unified model for hybrid filtering with learning algorithms which extracts customer's current browsing patterns and forms group of customersusing different clustering algorithms to obtain implicit users rating forrecommended product. In this research following three novel recommender systems are proposed. These systems are used to investigate issues and challenges related to recommendersystems. Hybrid web personalized recommender system based on web usagemining (HWPRS). Hybrid web personalized recommender system using centeringbunchingbased clustering (CBBCHPRS). Hybrid Fuzzy personalized recommender system using Modified Fuzzyc-means clustering (MFCMHFRS). 140 pp. Englisch.

Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Shinde Subhash K.Dr. Subhash K. Shinde is a Professor at Lokmanya Tilak College of Engineering, Navi Mumbai. He completed his Ph.D. ( Computer Engineering) in October 2012 from SRTM,Nanded, India. He is Chairman, B.O.S. in Computer .

Taschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - This book is to propose an adaptive recommendation model with learning algorithms, which increases web user satisfaction and save on thecosts of content management with minimal human intervention. This researchwork explores a unified model for hybrid filtering with learning algorithms which extracts customer's current browsing patterns and forms group of customersusing different clustering algorithms to obtain implicit users rating forrecommended product. In this research following three novel recommender systems are proposed. These systems are used to investigate issues and challenges related to recommendersystems. Hybrid web personalized recommender system based on web usagemining (HWPRS). Hybrid web personalized recommender system using centeringbunchingbased clustering (CBBCHPRS). Hybrid Fuzzy personalized recommender system using Modified Fuzzyc-means clustering (MFCMHFRS).