This paper describes in detail the web data mining technology, analyzes the relationship between the data on the web site to the tourism electronic commerce （including the server log, tourism commodity database, user...This paper describes in detail the web data mining technology, analyzes the relationship between the data on the web site to the tourism electronic commerce （including the server log, tourism commodity database, user database, the shopping cart）, access to relevant user preference information for tourism commodity. Based on these models, the paper presents recommended strategies for the site registered users, and has had the corresponding formulas for calculating the current user of certain items recommended values and the corresponding recommendation algorithm, and the system can get a recommendation for user.展开更多
With the rapid development of China＇s reform and opening up and the socialist market economy, the development of Internet technology has promoted the prosperity of e-commerce, and further promoted the rapid developme...With the rapid development of China＇s reform and opening up and the socialist market economy, the development of Internet technology has promoted the prosperity of e-commerce, and further promoted the rapid development of China＇s economy. Data mining technology is an advanced science and technology, which has important implications for the e-commerce data processing. Through the summary of the data mining technology, this article puts forward the application of data mining technology in electronic commerce, in order to better promote the development of electronic commerce.展开更多
Customers are of great importance to E-commerce in intense competition.It is known that twenty percent customers produce eighty percent profiles.Thus,how to find these customers is very critical.Customer lifetime valu...Customers are of great importance to E-commerce in intense competition.It is known that twenty percent customers produce eighty percent profiles.Thus,how to find these customers is very critical.Customer lifetime value(CLV) is presented to evaluate customers in terms of recency,frequency and monetary(RFM) variables.A novel model is proposed to analyze customers purchase data and RFM variables based on ordered weighting averaging(OWA) and K-Means cluster algorithm.OWA is employed to determine the weights of RFM variables in evaluating customer lifetime value or loyalty.K-Means algorithm is used to cluster customers according to RFM values.Churn customers could be found out by comparing RFM values of every cluster group with average RFM.Questionnaire is conducted to investigate which reasons cause customers dissatisfaction.Rank these reasons to help E-commerce improve services.The experimental results have demonstrated that the model is effective and reasonable.展开更多
文摘This paper describes in detail the web data mining technology, analyzes the relationship between the data on the web site to the tourism electronic commerce （including the server log, tourism commodity database, user database, the shopping cart）, access to relevant user preference information for tourism commodity. Based on these models, the paper presents recommended strategies for the site registered users, and has had the corresponding formulas for calculating the current user of certain items recommended values and the corresponding recommendation algorithm, and the system can get a recommendation for user.
文摘With the rapid development of China＇s reform and opening up and the socialist market economy, the development of Internet technology has promoted the prosperity of e-commerce, and further promoted the rapid development of China＇s economy. Data mining technology is an advanced science and technology, which has important implications for the e-commerce data processing. Through the summary of the data mining technology, this article puts forward the application of data mining technology in electronic commerce, in order to better promote the development of electronic commerce.
基金supported by the Natural Science Foundation under Grant Nos.71273139,60804047the Social Science Foundation of Chinese Ministry of Education under Grant No.12YJC630271
文摘Customers are of great importance to E-commerce in intense competition.It is known that twenty percent customers produce eighty percent profiles.Thus,how to find these customers is very critical.Customer lifetime value(CLV) is presented to evaluate customers in terms of recency,frequency and monetary(RFM) variables.A novel model is proposed to analyze customers purchase data and RFM variables based on ordered weighting averaging(OWA) and K-Means cluster algorithm.OWA is employed to determine the weights of RFM variables in evaluating customer lifetime value or loyalty.K-Means algorithm is used to cluster customers according to RFM values.Churn customers could be found out by comparing RFM values of every cluster group with average RFM.Questionnaire is conducted to investigate which reasons cause customers dissatisfaction.Rank these reasons to help E-commerce improve services.The experimental results have demonstrated that the model is effective and reasonable.
