TV series recommendation using fuzzy inference system, K-Means clustering and adaptive neuro fuzzy inference system

Recommending TV Series is a more challenging task than movie recommendation. Not only the system should consider the taste of the user, it has to take into account the time commitment factor because a TV series can contain thousands of episodes. This paper proposes a way of recommending TV series by analyzing the users' genre preferability of movies, the genre of the TV series and the number of episodes. This system analyzes the genre preferability of the user from movie data using Fuzzy Inference System, puts the users of similar taste into a cluster using K-Means and finally applies Adaptive Fuzzy Neuro Inference System in the cluster to predict the rating of that TV series the user might give in real life.