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A social hybrid recommendation system using LSTM and CNN

With the ever‐increasing use of Internet and social networks that generate a vast amount of information, there is a serious need for recommendation systems. In this article, we propose a recommender system utilizing deep neural networks that simultaneously considers both the users' ratings to the movies and the visual features of the movie poster and trailer. For this purpose, a hybrid movie recommender system, RSLCNet, has been developed using CNN and LSTM architectures. The proposed system considers the dynamics of users' interests in the collaborative filtering engine using the LSTM network that receives user‐rating sequences. In the content‐based filtering engine, utilizing CNN, the visual features of movie posters and trailers are extracted, and along with the actors and the directors, similar movies are recommended to the user. Moreover, each user's social influence is calculated employing the social information available on the user's Twitter account and used in the average movie rating to improve the effectiveness of the content‐based filtering part. The required datasets have been collected from MovieTweetings, Mise‐en‐scène, and OMDB. The evaluation results show that the accuracy and effectiveness of the proposed approach have been improved in terms of MAE and RMSE compared to the best available methods.