Towards Personalized Hybrid Recommender System using Average Visit Intervals

The overload of online data creates a problem in filtering information that is appropriate to a user, which is why many companies and websites utilize recommendation systems. Finding products and contents that match users' interests has become crucial for online content-providers, and users are always in search for content that suit their preferences. It is becoming more and more difficult for users to find the content they are looking for with the rate of content increasing in the world, thus the importance of recommender systems has increased more than ever. Yet, recommendation systems have not been perfected and shows much room for research and development. This paper therefore presents a new hybrid method, integrating a parallel collaborative and content-based filtering system with a ranking system based on a user profile with the addition of a time variable to represent the time sensitive characteristic of content and interest. The evaluation of our approach shows promising results in the cinematographic field and indicates further potential for development.