Hybrid Naive Bayes Classifier Weighting and Singular Value Decomposition Technique for Recommender System

Author

Puntheeranurak, Sutheera ; Sanprasert, Supitchaya

Author_Institution

Fac. of Eng., King Mongkut´´s Inst. of Technol. Ladkrabang, Bangkok, Thailand

fYear

2011

fDate

15-17 July 2011

Firstpage

473

Lastpage

476

Abstract

Recommender System is tool for recommending products or services to customers which helps increase circulation products in electronic commerce systems. This paper proposes Naive Bayes Classifier Weighing Technique that applies to use with Singular Value Decomposition Technique for solving sparsity problem. The comparison Mean Absolute Error (MAE) between Hybrid Naive Bayes Classifier Weighing and Singular Value Decomposition Technique (HNBW-SVD) and Pure Singular Value Decomposition Technique (SVD) that found HNBW-SVD yield lower MAE than SVD.

Keywords

Bayes methods; customer services; electronic commerce; pattern classification; recommender systems; singular value decomposition; customer products; customer services; electronic commerce systems; hybrid Naive Bayes classifier weighting technique; mean absolute error; recommender system; singular value decomposition technique; sparsity problem solving; Accuracy; Collaboration; Matrix decomposition; Prediction algorithms; Recommender systems; Singular value decomposition; Naive Bayes Classifier Weighing Technique; Recommender System; Singular Value Decomposition Technique;

fLanguage

English

Publisher

ieee

Conference_Titel

Software Engineering and Service Science (ICSESS), 2011 IEEE 2nd International Conference on

Conference_Location

Beijing

Print_ISBN

978-1-4244-9699-0

Type

conf

DOI

10.1109/ICSESS.2011.5982356

Filename

5982356