Recurrent Neural Networks for Recommender Systems

The Internet is becoming one of the biggest sources of information in recent years, keeping people updated about everyday events. The information available on it is also growing, with the increase in the use of the Internet. Due to this, it takes a great deal of time and effort to locate relavent knowledge that the user wants. Recommender systems are software mechanisms that automatically suggest relavent user-needed information. Recurrent Neural Networks has lately gained importance in the field of recommender systems, since they give improved results in building deep learning models with sequential data. Unlike conventional recommendation models, RNN models more easily capture irregular and complex user-item relations. This paper provides a thorough analysis of the research content of recommendation systems based on RNN models.