Social movie recommender system based on deep autoencoder network using Twitter data

Recommender systems attempt to provide effective suggestions to each user based on their interests and behaviors. These recommendations usually match the personal user preferences and assist them in the decision-making process. With the ever-expanding growth of information on the web, online education systems, e-commerce, and, eventually, the emergence of social networks, the necessity of developing such systems is unavoidable. Collaborative filtering and content-based filtering are among the most important techniques used in recommender systems. Meanwhile, with the significant advances in deep learning in recent years, the use of this technology has been widely observed in recommender systems. In this study, a hybrid social recommender system utilizing a deep autoencoder network is introduced. The proposed approach employs collaborative and content-based filtering, as well as users' social influence. The social influence of each user is calculated based on his/her social characteristics and behaviors on Twitter. For the evaluation purpose, the required datasets have been collected from MovieTweetings and Open Movie Database. The evaluation results show that the accuracy and effectiveness of the proposed approach have been improved compared to the other state-of-the-art methods.

Automated summary

Recommender systems aim to provide personalized suggestions based on user interests and behaviors, with collaborative filtering and deep learning being commonly used techniques. The study introduces a hybrid social recommender system utilizing a deep autoencoder network, which improves recommendation accuracy and effectiveness by incorporating social influence. The proposed approach combines collaborative and content-based filtering, along with analyzing users' social characteristics and behaviors for better recommendations.