Design of a Recommender System for Online Shopping using Decision Tree and APRIORI Algorithm: Case Study in Digikala Store

With the growing data available on the Internet, customization of the web sites information has become a requirement for users. A procedure for the appropriate customization of web data is configured by automatic extraction of combined knowledge of the log file and user profile information. In this paper, integrating decision tree and association rules for user profile information and log information of website in an online shopping store is targeted. The tangible results of such a framework for decision makers and marketers are customization of web pages and statistical analysis for sale improvement. Applying association rules the website users’ patterns are mined and utilizing decision tree users are classified and their interests are determined. By combining the results of two algorithms and its analysis, the behavior models from user profile, user interests in terms of age and gender, and the most visited web pages by subject can be achieved.