DocumentCode :
2999319
Title :
E-commerce Recommendation Method Based on Genetic Algorithm and Composite Weight Matrix
Author :
Dian, He ; Ying, Liang
Author_Institution :
Sch. of Comput. & Electron. Eng., Hunan Univ. of Commerce, Changsha, China
fYear :
2010
fDate :
25-27 June 2010
Firstpage :
2760
Lastpage :
2763
Abstract :
Accounting for the characteristics of E-commerce Website personal service and the features of users´ and goods´ similarities distribution, an E-commerce recommendation method based on clustering using genetic algorithm is designed. By using a composite weight matrix to integrate the situation of users purchasing, this method improves the result of clustering, and the result of clustering reflects the similarities of users and goods more accurately. This method is accorded with the reality of E-commerce Website personal service and is perfect for users´ and goods´ clustering computing on E-commerce Website recommendation.
Keywords :
Web sites; electronic commerce; genetic algorithms; goods distribution; pattern clustering; recommender systems; Website personal service; clustering method; composite weight matrix; e-commerce; genetic algorithm; good distribution; recommendation method; Business; Computational modeling; Computers; Convergence; Genetics; Optimization; Pattern recognition; Clustering; E-commerce Personal Service; Genetic Algorithm; Web Mining;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Electrical and Control Engineering (ICECE), 2010 International Conference on
Conference_Location :
Wuhan
Print_ISBN :
978-1-4244-6880-5
Type :
conf
DOI :
10.1109/iCECE.2010.674
Filename :
5630841
Link To Document :
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