Behavior-based video recommendation using adaptive neuro-fuzzy system on social TV

User-based collaborative filtering often considers a set of users who rated on a target item and computes similarities between other users and the target user to select his/her neighbors, then extrapolates the target user's rating from the neighbors' ratings. This traditional approach uses only the neighbors' ratings for recommendation measurement. However, according to our study, dissimilar users whose ratings still significantly influence to the target user's rating prediction. In addition, to choose a video to watch, a user often takes in to consideration multi criteria. We analyze users' behavior to choose a video. They often explore genres or tags, then read abstraction before choosing a video to watch. Therefore, their ratings and the information of a video have a strong correlation. Therefore, based on the fuzzy neural network, a new collaborative filtering method for video recommendation is proposed. Here, the fuzzy neural network is used to learn users' ratings with respect to their behaviors. The proposal here is to adjust a model of the neural network with input is users' behavior and output is their ratings for each target video. Concretely, the behavior of a user (or user profile) is learned by the users' ratings and the information of the corresponding videos. In addition, for each target video, all users' profile who made ratings on it will be collected. Then each profile is treated as an input of the fuzzy neural network and the corresponding rating value is treated as output of the fuzzy neural network. The rating of a user on the target video will be predicted based on the trained neural network. The experiments with netflix dataset reveals that the proposed method is a significantly effective approach.