Design a Hybrid Recommender System Solving Cold-start Problem Using Clustering and Chaotic PSO Algorithm

One of the main challenges of increasing information in the new era, is to find information of interest in the mass of data. This important matter has been considered in the design of many sites that interact with users. Recommender systems have been considered to resolve this issue and have tried to help users to achieve their desired information; however, they face limitations. One of the most important challenges that they face is cold-start problem, which is raised when a new user/item entered into the system, while no previous information is available for it. The lack of previous knowledge of the new user/item, will causes the system fails generating its suggestions normally. In this paper, to solve the problem of cold-start user/item a new method is presented using combining content-based models and collaborative filtering. Moreover, demographic data is used to recommend the nearest items to cold-start users/items' interests. Compared to existing methods, the evaluation results show that the proposed method reduces the MAE and RMSE error.