Recommender System for Audio Recordings: A New Approach Devised for Recommendation of Audio Recording Items - Couverture souple

Lee, Jong Seo; Dekhtyar, Alexander

 
9783846519363: Recommender System for Audio Recordings: A New Approach Devised for Recommendation of Audio Recording Items
Couverture souple
ISBN 10 : 3846519367 ISBN 13 : 9783846519363
Editeur : LAP LAMBERT Academic Publishing, 2011

Synopsis

Nowadays the largest E-commerce or E-service websites offer millions of products for sale. A Recommender system is defined as software used by such websites for recommending product items to users according to the users’ tastes. In this project, we develop a recommender system for a private multimedia web service company. In particular, we devise three recommendation engines using different data filtering methods – named weighted-average, K-nearest neighbors, and item-based – which are based on collaborative filtering techniques, which work by recording user preferences on items and by anticipating the future likes and dislikes of users by comparing the records, for prediction of user preference. To acquire proper input data for the three engines, we retrieve data from database using three data collection techniques: active filtering, passive filtering, and item-based filtering. For experimental purpose we compare prediction accuracy of those recommendation engines with the results from each engine and additionally we evaluate the performance of weighted-average method using an empirical analysis approach – a methodology which was devised for verification of predictive accuracy.

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Présentation de l'éditeur

Nowadays the largest E-commerce or E-service websites offer millions of products for sale. A Recommender system is defined as software used by such websites for recommending product items to users according to the users’ tastes. In this project, we develop a recommender system for a private multimedia web service company. In particular, we devise three recommendation engines using different data filtering methods – named weighted-average, K-nearest neighbors, and item-based – which are based on collaborative filtering techniques, which work by recording user preferences on items and by anticipating the future likes and dislikes of users by comparing the records, for prediction of user preference. To acquire proper input data for the three engines, we retrieve data from database using three data collection techniques: active filtering, passive filtering, and item-based filtering. For experimental purpose we compare prediction accuracy of those recommendation engines with the results from each engine and additionally we evaluate the performance of weighted-average method using an empirical analysis approach – a methodology which was devised for verification of predictive accuracy.

Biographie de l'auteur

Jong Seo Lee is a software engineer in San Francisco Bay Area, USA. He graduated Cal Poly - San Luis Obispo with computer science Master's degree. His interest includes software engineering, DBMS, machine learning, data filtering and manipulation, and information systems.

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

Éditeur
LAP LAMBERT Academic Publishing
Date d'édition
2011
Langue
anglais
ISBN 10 
3846519367
ISBN 13 
9783846519363
Reliure
Broché
Nombre de pages
84
Coordonnées du fabricant
non disponible
Personne responsable
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