A new model for assessing the role of customer behavior history, product classification, and prices on the success of the recommender systems in e commerce
Purpose This paper aims to propose a new method for evaluating the success of the recommender systems based on customer history, product classification and prices criteria in the electronic commerce. To evaluate the validity of the model, the structural equation modeling technique is employed. Design/methodology/approach A method has been suggested to evaluate the impact of customer history, product classification and prices on the success of the recommender systems in electronic commerce. After that, the authors investigated the relationship between these factors. To achieve this goal, the structural equation modeling technique was used for statistical conclusion validity. The results of gathered data from employees of a company in Iran is indicated the impact of the customer history on the success of recommender systems in e commerce which is related with the user profile, expert opinion, neighbors, loyalty and clickstream. These factors positively influence the success of recommender systems in ecommerce. Findings The obtained results demonstrated the efficiency and effectiveness of the proposed model in term of the success of the recommender systems in the electronic commerce. Originality/value In this paper, the effective factors of success of recommender systems in electronic commerce are pointed out and the approach to increase the efficiency of this system is applied into a practical example.
Kybernetes