MovRec: a personalized movie recommendation system for children based on online movie features

The purpose of this study is to suggest suitable movies for children among the various multimedia selections available these days. Multimedia have a significant impact on the social and psychological development of children who are often explored to inappropriate materials, including movies that are either accessible online or through other multimedia channels. Even though not all movies are bad, there are negative effects of offensive languages, violence and sexuality as exhibited in movies. Parents and guidance of children need all the help they can get to promote the healthy use of movies these days.

To offer parents appropriate movies of interest to their youths, the authors have developed MovRec, a personalized movie recommender for children, which is designed to provide educational and suitable entertaining opportunities for children. MovRec determines the appealingness of a movie for a particular user based on its children-appropriate score computed by using the backpropagation model, pre-defined category using latent Dirichlet allocation, its predicted rating using matrix factorization and sentiments based on its users’ reviews, which along with its like/dislike count and genres, yield the features considered by MovRec. MovRec combines these features by using the CombMNZ model to rank and recommend movies.

The performance evaluation of MovRec clearly demonstrates its effectiveness and its recommended movies are highly regarded by its users.

Unlike Amazon and other online movie recommendation systems, such as Common Sense Media, Internet Movie Database and TasteKid, MovRec is unique, as to the best of the authors’ knowledge, MovRec is the first personalized children movie recommender.