A product recommendation system for solving the cold start problem

Abstract

The recommendation of product or service based on application needs different product attributes and user requirements to be analyzed. But if both the kinds of information are unavailable then such kind of issue in the recommendation system is known as the clod start problem.[1] In this presented work, the effort is made in order to understand and resolve the issue of recommendation system. Thus, a four phase recommendation system is introduced in this work. In first phase, the web usage data is preprocessed which is used for further recommendations of products. In next phase, the frequent pattern based recommendation is performed. In third phase, the recommendation is made on the basis of user current search (user click streams) and similar user behaviors available in web access logs. Additionally, the final recommendation is made on the basis of filtering the results obtained in third phase and the cost, brand and social review of the product or service. The implementation of the system is performed on JAVA technology and the results in terms of accuracy, error rate is computed. The results show the prediction is accurate and required less computational resources. Thus, in recommendation system design the model is acceptable for use and future extension of work for solving the issue of cold start problem.