Movie Recommendation System with Fake Review Detection Using Deep Learning

With the rapid development of network technology and entertainment creation, the diversity of movie genres has expanded significantly. Users often face the challenge of selecting movies that align with their preferences. Deep learning, a field that has garnered substantial attention from scholars, offers the potential to model more complex structures due to its deep architecture. This study proposes a personalized movie recommendation system integrating deep learning for enhanced accuracy and tailored suggestions. We combine personalized recommendation and collaborative filtering algorithms, leveraging users’ historical behaviors and big data from the mobile Internet and social media. Collaborative filtering techniques are employed within the context of movie recommendations. Specifically, we utilize the Seq2Seq model based on LSTM recurrent neural networks for deep learning. We also address the issue of fake reviews by integrating deep learning techniques to identify and filter out unreliable reviews. Overall, our system aims to create a more reliable and enjoyable movie-watching experience for users.