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ISSN 2063-5346
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MOVIE RECOMMENDATION SYSTEM USING DEEP LEARNING MODEL

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Mahesh Sankaran, Dr E.N Ganesh
» doi: 10.31838/ecb/2023.12.s3.092

Abstract

Recommendation frameworks have become widespread of-late because they manage the data overload problem by recommending the most important items to customers through a smorgasbord of information. As for the media item, online collaborative movie propositions make efforts to help customers get preferred motion pictures by accurately capturing relative neighbors among customers or by capturing motion pictures from their verifiable normal ratings. However, due to the lack of information, the rapid expansion of movies and customers makes choosing neighbors more complicated. In this paper, to develop Principal Component Analysis with Adaptive Deep Learning Model (PCAADLM) for automatic movie recommendation system. The projected technique is developed to identify the best rated movies and automatic movie recommendation system. This PCAADLM is a combination of Recurrent Neural Network-Long Short-Term Memory (RNN-LSTM), Principal Component Analysis (PCA) and Cat and Mouse based Optimizer (CMO). In the RNN-LSTM, the CMO is utilized to select optimal weighting parameters. The PCA is utilized along with proposed techniques to enable efficient movie recommendation system. To validate the proposed methodology, the movie databases is gathered from the online solutions. The proposed methodology is executed in MATLAB in addition performances can be assessed by performance measures like recall, precision, accuracy, recall, specificity, sensitivity and F_Measure. The projected methodology can be compared with the conventional methods such as SDLM, ODLM, Recurrent Neural Network (RNN) and Artificial Neural Network (ANN) respectively.

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