- author = "Alireza Kazeminia and Marjan Kaedi and Beenazir Ganji",
- title = "Personality-Based Personalization of Online Store Features Using Genetic Programming: Analysis and Experiment",
- journal = "Journal of Theoretical and Applied Electronic Commerce Research",
- year = "2019",
- volume = "14",
- number = "1",
- pages = "16--29",
- month = jan,
- keywords = "genetic algorithms, genetic programming, Personalization, Online shopping, Personality, Decision-making style",
- publisher = "Universidad de Talca, Chile",
- ISSN = "0718-1876",
-
URL = "
http://www.jtaer.com/portada.php?agno=2019&numero=1#",
-
URL = "http://www.jtaer.com/statistics/download/download.php?co_id=JTA20190102",
-
DOI = "
doi:10.4067/S0718-18762019000100103",
- size = "14 pages",
- abstract = "The decisions made by the customers in online environments are influenced by their personality characteristics. Each customer in an online environment relies more heavily on certain features of a store to make decisions while ignoring others. Thus, personalizing these features may streamline the decision-making process and increase satisfaction. In this paper, an intelligent method for personalizing the features of an online store according to the users personality is presented. In the proposed method, using genetic programming several equations are developed to estimate how users with different personality characteristics prefer various features of an online store. These equations are then used for personalization of the store features to increase customers satisfaction and persuade them to make larger purchases. The evaluation on a sample of 194 individuals indicates that the obtained equations are able to estimate the users preferences with over 80percent accuracy in most cases. In addition, empirical assessment of the obtained equations shows that the proposed personalization method improves the user satisfaction.",
-
notes = "University of Isfahan, Faculty of Computer
Engineering, Isfahan, Iran
www.jtaer.com",
