A method for solving the cold start problem in recommendation systems

Recommendation systems become essential in web applications that provide mass services, and aim to suggest automatic items (services) of interest to users. The most popular used technique in such systems is the collaborative filtering (CF) technique, which suffer from some problems such as the cold-start problem, the privacy problem, the user identification problem, the scalability problem, etc. In this paper, we address the cold-start problem by giving recommendations to any new users who have no stored preferences, or recommending items that no user of the community has seen yet. While there have been lots of studies to solve the cold start problem, but it solved only item-cold start, or user-cold start, also provided solutions still suffer from the privacy problem. Therefore, we developed a privacy protected model to solve the cold start problem (in both cases user and item cold start). We suggested two types of recommendation (node recommendation and batch recommendation), and we compared the suggested method with three other alternative methods (Triadic Aspect Method, Naïve Filterbots Method, and MediaScout Stereotype Method), and we used dataset collected from online web news to generate recommendations based on our method and based on the other alternative three methods. We calculated level of novelty, coverage, and precision. We found that our method achieved higher level of novelty in the batch recommendation whilst it achieved higher levels of coverage and precision in the node recommendations technique comparing to these three methods.