Development of E-Commerce Recommendation System Based on FP-Growth Algorithm

Association rule mining is a process of finding frequent patterns among database, relational database and other item sets of large data. FP-Growth algorithm is one of the typical recommendation algorithms based on association rules. Firstly, the relational rules and FP-Growth algorithm are studied. Then, the electronic commerce recommendation system is designed, including the overall structure design and core function design. Then, the implementation of FP-Growth mining algorithm is studied, and the implementation flow of constructing FPtree and FP-Growth algorithm is designed. Finally, the system is tested with real e-commerce data, and the test results are output according to the steps of reading initial e-commerce data, calculating support, arranging by frequency, rewriting item set in transaction database, constructing FPtree and generating frequent item set. The main contribution of this paper is to deeply study the FP-Growth algorithm and provide a complete solution for the application of FP-Growth algorithm in e-commerce system.