Predicting Customer Favorite Products in E-commerce
Publish Year: 1399
Type: Conference paper
Language: English
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Document National Code:
ICIRES06_008
Index date: 26 July 2020
Predicting Customer Favorite Products in E-commerce abstract
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.
Predicting Customer Favorite Products in E-commerce Keywords:
Customer shopping basket analysis , Matrix factorization , Logistic regression , Apriori algorithm , Online AdaBoost.
Predicting Customer Favorite Products in E-commerce authors
Bita Ture Savadkoohi
Seraj Higher Education Institute, Department of Computer and Electrical Engineering, Next to Munciple Museum, Maghsoodieh Avenue, Mohammadi Alley, Tabriz, Iran
Mitra Aliakbari
Azad University, Azarshahr Branch, Department of Computer and Electrical Engineering, Azarshahr, Iran