Title :
Collaborative filtering recommendation algorithm based on item attributes
Author :
Mengxing Huang ; Longfei Sun ; Wencai Du
Author_Institution :
Coll. of Inf. Sci. &Technol, Hainan Univ., Haikou, China
fDate :
June 30 2014-July 2 2014
Abstract :
Aiming at the shortcomings of datasets sparsity and cold start in the traditional Item-based collaborative filtering recommendation algorithm, to improve the calculating accuracy of similarity and recommendation quality, taking attribute theory as theoretical basis, a collaborative filtering recommendation algorithm based on item attributes is proposed. Through analyzing the items, the attributes are listed and attribute weights are calculated, the similarity between items is calculated by taking advantage of attribute barycenter coordinate model and item attribute weights, and then produce recommendations forecasts. Finally, the experimental results show that the compared with traditional algorithm the proposed algorithm can effectively alleviate the user rating data sparsity problem and improve the quality of recommendation system.
Keywords :
collaborative filtering; recommender systems; attribute barycenter coordinate model; attribute theory; collaborative filtering recommendation algorithm; item attribute weights; recommendation quality; similarity quality; user rating data sparsity problem; Accuracy; Collaboration; Educational institutions; Filtering; Motion pictures; Prediction algorithms; Vectors; attribute theory; collaborative filtering; item attributes; similarity;
Conference_Titel :
Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD), 2014 15th IEEE/ACIS International Conference on
Conference_Location :
Las Vegas, NV
DOI :
10.1109/SNPD.2014.6888678