E-commerce Product Networks, Word-of-mouth Convergence, and Product Sales

Driven by the network theory on status, we propose an interesting argument that network connection between two products affects their word-of-mouth (WOM) rating convergence and that WOM rating convergence affects their sales. To empirically validate this argument, we analyze data from China\u27s largest business-to-consumer platform, Tmall.com. After addressing potential endogeneity issues and performing various robustness checks to ensure the consistency of our findings in various ways, we found that network connection between two products via recommender systems was related to the convergence of WOM rating between the two products. Moreover, WOM rating convergence between two products was associated with a decrease in the sales quantity of the product with higher WOM rating, whereas it was associated with an increase in the sales quantity of the product with lower WOM rating. Overall, WOM rating convergence was associated with an increase in the total sales quantity of the two products. Our findings provide important theoretical contributions and notable implications for e-commerce product marketing and platform design

Application of Improved Collaborative Filtering in the Recommendation of E-commerce Commodities

Problems such as low recommendation precision and efficiency often exist in traditional collaborative filtering because of the huge basic data volume. In order to solve these problems, we proposed a new algorithm which combines collaborative filtering and support vector machine (SVM). Different with traditional collaborative filtering, we used SVM to classify commodities into positive and negative feedbacks. Then we selected the commodities that have positive feedback to calculate the comprehensive grades of marks and comments. After that, we build SVM-based collaborative filtering algorithm. Experiments on Taobao data (a Chinese online shopping website owned by Alibaba) showed that the algorithm has good recommendation precision and recommendation efficiency, thus having certain practical value in the E-commerce industry