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.