Web Recommendation System Based on Users Searching History - Couverture souple

Paliwal, Gaurav

 
9786202311335: Web Recommendation System Based on Users Searching History
Couverture souple
ISBN 10 : 6202311339 ISBN 13 : 9786202311335
Editeur : Scholars' Press, 2018

Synopsis

Presently, most of the available recommendation system uses collaborative filtering approach. These type of recommender systems assumes that if two users have shown similar interest on the same set of contents, they may show a similar interest pattern in choosing future contents. However, it may happen that the users who have certain tastes on one specific category of contents may behave differently in choosing contents from other categories. Also, the collaborative filtering approaches do not work efficiently with sparse data sets, where there are a small number of contents or a limited number of users in the content categories. To overcome all these problems, a novel approach of recommending contents across different categories by considering both the semantic information of contents and user interests is used. This approach uses Linked Data as the source to find the appropriate semantics of the contents extracted from users viewing history. The semantic concepts retrieved for the contents are then grouped together into semantic clusters based on their similarity and relevance.

Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.

Présentation de l'éditeur

Presently, most of the available recommendation system uses collaborative filtering approach. These type of recommender systems assumes that if two users have shown similar interest on the same set of contents, they may show a similar interest pattern in choosing future contents. However, it may happen that the users who have certain tastes on one specific category of contents may behave differently in choosing contents from other categories. Also, the collaborative filtering approaches do not work efficiently with sparse data sets, where there are a small number of contents or a limited number of users in the content categories. To overcome all these problems, a novel approach of recommending contents across different categories by considering both the semantic information of contents and user interests is used. This approach uses Linked Data as the source to find the appropriate semantics of the contents extracted from users viewing history. The semantic concepts retrieved for the contents are then grouped together into semantic clusters based on their similarity and relevance.

Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.

Éditeur
Scholars' Press
Date d'édition
2018
Langue
anglais
ISBN 10 
6202311339
ISBN 13 
9786202311335
Reliure
Broché
Nombre de pages
64
Coordonnées du fabricant
non disponible
Personne responsable
non disponible