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education.field_of_study, business.industry, Computer science, Population, Recommender system, Machine learning, computer.software_genre, Adaboost algorithm, Purchasing, Session (web analytics), AdaBoost, Artificial intelligence, business, education, and computer

The goal of ecommerce recommender systems is to increase sales by providing accurate recommendations to the users. However, users purchase intention plays a vital role in making a purchase and ignoring their desire to make a purchase before providing a recommendation can negatively influence recommendation accuracy because it does not target the specific users. The proposed model identifies eight factors that can influence a user’s purchase intention and categorises them as user and population behaviors. These factors are fed into the AdaBoost algorithm to predict a user’s purchase intention. The results show that we achieve high accuracy and the ROC area is 0.91, when we use the identified factors to predict a user’s purchase intention. The proposed model outperforms two state-of-the-art models that have been used to predict a user’s purchase intention.