In:
Applied Mechanics and Materials, Trans Tech Publications, Ltd., Vol. 475-476 ( 2013-12), p. 978-982
Abstract:
Recommendation systems have achieved widespread success in E-commerce nowadays. There are several evaluation metrics for recommender systems, such as accuracy, diversity, computational efficiency and coverage. Accuracy is one of the most important measurement criteria. In this paper, to improve accuracy, we proposed a hybrid recommender algorithm by an improved similarity method (ISM), combining demographic recommendation techniques and user-based collaborative filtering (CF) algorithms. Experiments were performed to compare the present approach with the other classical similarity measures based on the MovieLens dataset. The Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) values show the superiority of the proposed algorithm.
Type of Medium:
Online Resource
ISSN:
1662-7482
DOI:
10.4028/www.scientific.net/AMM.475-476
DOI:
10.4028/www.scientific.net/AMM.475-476.978
Language:
Unknown
Publisher:
Trans Tech Publications, Ltd.
Publication Date:
2013
detail.hit.zdb_id:
2251882-4