Movie recommendation system with hybrid collaborative and content-based filtering using convolutional neural network
Keywords:
movie recommendation system, open movie database, collaborative filtering, content-based filtering, convolutional neural networkAbstract
With the advancements of big data, recommendation systems have become extremely useful in wide applications such as e-business service, social networks, e-commerce, e-learning etc. Typically, Movie Recommendation systems predict which movie a user likes based on the characteristics of earlier watched/liked movies or interests and their likelihoods. This recommendation system gathers information from users and offers top movie recommendations. Various researchers use collaborative and content-based filtering recommender systems. However, as the number of movies and users grows, neighbor selection becomes more difficult due to data scarcity. Thus, the proposed approach uses hybrid collaborative and content-based filtering. A crossover social recommender structure utilizing a significant CNN based RELU network is introduced. The experimental results on Movie Tweetings and Open Movie Database dataset shows that the accuracy of the proposed approach has improved compared to the existing techniques.
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