Predicting Customer Favorite Products in E-commerce

With rapid development of information technology as well as, its growing trend, discovering patterns from the data bases has more and more attention in the recent years. On theother hand, customer shopping basket analysis is an important value in highly competitive nature of E-commerce market. As a result, it can help to discover interesting patterns to decision making process. For this aim, in this paper data preprocessing of data base is done with matrix factorization. Then, in order to investigate customer behavior and find favorite products in Ecommerce markets, logistic regression, Apriori algorithm’s confidence formula and online AdaBoost are applied. Thus, in this way, we have been able to collect customer’s favorites and suggest desire products to customers. As a result, the amount of sales in E-commerce will increase.

Keywords:

Customer shopping basket analysis , Matrix factorization , Logistic regression , Apriori algorithm , Online AdaBoost.