Abstract:
Choosing the best movie predict system is a necessary step for boosting user
experience and interest in current streaming service platforms. But all these existing
methods such as collaborative filtering have its own weakness like cold-start issues,
issues of scalability and the propensity of these methods to generate popular items.
Based on this, our study would try to take on this challenge by introducing a hybrid
recommendation algorithm that employs the advantages of different recommendation
methods. By doing so, we intend to improve recommendation accuracy, user
satisfaction, and overall platform performance. In response to the limitations of
existing movie recommendation systems, our study proposes a novel hybrid
recommendation system named "Alternating Least Square - Convolutional Neural
Network Recommendation" (ACR). ACR integrates the ALS matrix factorization
technique with the power of Convolutional Neural Network (CNN), specifically
utilizing VGG16, for image feature extraction. The anatomy of ACR involves
incorporation of additional elements into the ALS matrix factorization model by
emphasizing the effect of convolutional neural networks and specifically the VGG16
architecture for feature extraction. In developing our methodology, we have utilized
the strengths of collaborative filtering as well as content analysis to improve
recommendation accuracy and user satisfaction and the performance of the platform.
With this, our RMSE was computed at 0. 8315 by using ACR.
