The design and implementation of Feature-Grading recommendation system for e-commerce

Abstract

In this paper we present a novel approach named Feature-Grading which is a comprehensive algorithm used to make recommendation of commodities in e-commerce business. It is a technique based on the integration of feature mining, sentimental analysis, and the records of customer historical behaviors. The overall process of Feature-Grading can be separated into 5 key steps: 1.Extracting overall feature set of a group category of commodities; 2.Extracting modifier set and negative words set; 3.Acquiring specific feature set and feature assessment set; 4.Acquiring specific feature weight set; 5.Acquiring item weight set. After these 5 steps, we are able to grade and rank all the items with an acquired grading equation. Then the needed as well as top ranking items can be recommended. Moreover, we utilize the real information of mobiles and their reviews from the famous e-commerce website Amazon.cn as our experimental data and discuss some important results which reveal that the Feature-Grading really works well. At last, we also briefly introduce the prototype recommendation system we developed on the basis of Feature-Grading.