Improving the performance of recommender systems in the face of the cold start problem by analyzing user behavior on social network

The goal of recommender system is to provide desired items for users. One of the main challenges affecting the performance of recommendation systems is the cold-start problem that is occurred as a result of lack of information about a user/item. In this article, first we will present an approach, uses social streams such as Twitter to create a behavioral profile, then user profiles are clustering with machine learning techniques. Based on this clustering, predictions are made using machine learning techniques such as the Random Forest algorithm (RF) and the Gradient Boosting algorithm (GB). Therefore, the user is not required to provide any kind of data explicitly anymore. As a result of this method, cold start problem will decrease among users' social networks. Because the system uses this data to create user profiles, this will be an input for recommender systems. Numerous experiments have been performed in this field and compared to some new cold start algorithms; very satisfactory results have been obtained. In this paper, we have concluded that the clustering process greatly increases the performance accuracy of the models and reduces the average absolute error, and also the Gradient Boosting algorithm has a better performance than the Random Forest algorithm.