2022
DOI: 10.1109/access.2022.3168161
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Movie Popularity and Target Audience Prediction Using the Content-Based Recommender System

Abstract: The movie is one of the integral components of our everyday entertainment.The worldwide movie industry is one of the most growing and significant industries and seizing the attention of people of all ages. It has been observed in the recent study that only a few of the movies achieve success. Uncertainty in the sector has created immense pressure on the film production stakeholder. Moviemakers and researchers continuously feel it necessary to have some expert systems predicting the movie success probability pr… Show more

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Cited by 30 publications
(12 citation statements)
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“…Because CNN quickly picks up new features, there is less need for manual feature extraction. It is also possible to retrain CNN to carry out new tasks for which it was previously trained [ 33 ].…”
Section: Methodsmentioning
confidence: 99%
“…Because CNN quickly picks up new features, there is less need for manual feature extraction. It is also possible to retrain CNN to carry out new tasks for which it was previously trained [ 33 ].…”
Section: Methodsmentioning
confidence: 99%
“…CNN reduces the requirement for manual features extraction because the network learns the features immediately. CNN can be retrained to do new tasks that build on previously trained networks [ 41 ]. LSTM is a type of recurrent neural networks (RNN) that can learn long-term dependencies.…”
Section: Methodsmentioning
confidence: 99%
“…CNN reduces the requirement for manual features extraction because the network learns the features immediately. CNN can be retrained to do new tasks that build on previously trained networks [ 41 ].…”
Section: Methodsmentioning
confidence: 99%
“…Subsequent research endeavors to incorporate knowledge graphs with additional data sources such as social networks and user feedback, investigate recommender systems based on reinforcement learning, and expand the approach to diverse industries like news, e-commerce, and music. S. Sahu, R. Kumar, M. S. Pathan, J. Shafi, Y. Kumar and M. F. Ijaz [10], the study offers an expert method to assist in decision-making and tackles the problem of forecasting movie success early in the production process. The study forecasts target audience preferences and movie popularity using deep learning models and content-based movie recommendation systems.…”
Section: Literature Reviewmentioning
confidence: 99%