ISBN: 9783330335165 - real time shop-type recommendation system (7 risultati)

Paperback. Condizione: Brand New. 80 pages. 8.66x5.91x0.19 inches. In Stock.

Taschenbuch. Condizione: Neu. Real Time Shop-Type Recommendation System | Abhijeet A. Chincholkar (u. a.) | Taschenbuch | 80 S. | Englisch | 2019 | LAP LAMBERT Academic Publishing | EAN 9783330335165 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu. …

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Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book deals with Real Time shop-type recommendation system, to generate the appropriate Real Time shop-type recommendations based on popularity prediction a feature fusion matrix factorization method is suggested. When an investor/shop owner decides to open a new shop on his available place and if there are various shops of different types, then Real Time shop-type recommendation can be done in following way: The location features fs and the commercial features Cst of each candidate type are required to be captured first. The set of feature values extracted for selected type t are given as a input to the feature fusion matrix factorization model. The popularity value for each type is calculated by using extracted feature values and value obtained by sentiment analysis. The predicted popularity pst is based upon the value of the selected type t and feature values of every shop type from selected circular area with radius r. In this way, for each shop type in the given location, first of all its popularity prediction is to be done. Then, as per the popularity values, all shop types are ranked in descending order and the shop types. 80 pp. Englisch.…

Taschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - This book deals with Real Time shop-type recommendation system, to generate the appropriate Real Time shop-type recommendations based on popularity prediction a feature fusion matrix factorization method is suggested. When an investor/shop owner decides to open a new shop on his available place and if there are various shops of different types, then Real Time shop-type recommendation can be done in following way: The location features fs and the commercial features Cst of each candidate type are required to be captured first. The set of feature values extracted for selected type t are given as a input to the feature fusion matrix factorization model. The popularity value for each type is calculated by using extracted feature values and value obtained by sentiment analysis. The predicted popularity pst is based upon the value of the selected type t and feature values of every shop type from selected circular area with radius r. In this way, for each shop type in the given location, first of all its popularity prediction is to be done. Then, as per the popularity values, all shop types are ranked in descending order and the shop types.…

Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Chincholkar Abhijeet A.Abhijeet A. Chincholkar works as Assistant Professor in Electronics & Telecommunication Engineering Department of Jagadambha College of Engineering & Technology, Yavatmal. He has 10 years of teaching experience.…

Taschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -This book deals with Real Time shop-type recommendation system, to generate the appropriate Real Time shop-type recommendations based on popularity prediction a feature fusion matrix factorization method is suggested. When an investor/shop owner decides to open a new shop on his available place and if there are various shops of different types, then Real Time shop-type recommendation can be done in following way: The location features fs and the commercial features Cst of each candidate type are required to be captured first. The set of feature values extracted for selected type t are given as a input to the feature fusion matrix factorization model. The popularity value for each type is calculated by using extracted feature values and value obtained by sentiment analysis. The predicted popularity pst is based upon the value of the selected type t and feature values of every shop type from selected circular area with radius r. In this way, for each shop type in the given location, first of all its popularity prediction is to be done. Then, as per the popularity values, all shop types are ranked in descending order and the shop types.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 80 pp. Englisch.…