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Vol. 9, No. 2, May 2024

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This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

Content-based Filtering Movie Recommender System Using Semantic Approach with Recurrent Neural Network Classification and SGD

https://doi.org/10.22219/kinetik.v9i2.1940
Adinda Arwa Salsabil
Telkom University
Erwin Budi Setiawan
Telkom University
Isman Kurniawan
Telkom University

Corresponding Author(s) : Adinda Arwa Salsabil

Adindasalsabil359@gmail.com

Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, Vol. 9, No. 2, May 2024

Abstract

The application of recommendation systems has been applied in various types of platforms, especially applications for watching movies such as Netflix and Disney+. The recommendation system is purposed to make it easier for users, especially in choosing a movie because currently the number of movie productions is increasing every day. This research proposed a CBF movie recommendation system by comparing the performance of several semantic methods to be able to get the best rating prediction results. In order to improve the performance quality to get the best rating prediction results, this research  utilized semantic feature methods by comparing the performance of the evaluation results produced by the TF-IDF method and word embedding applications, such as BERT, GPT-2, RoBERTa, and implemented RNN model to classify the results of rating prediction. The data were used to generate the recommendation system by involving 854 data movie and 39 accounts with a total of 34,056 movie reviews on Twitter. This research has succeeded in getting a method that produced rating predictions, namely RoBERTa. In the classification process with the RNN model and SGD optimization, the measurement results with confusion matrix by classifying the RoBERTA rating prediction obtained an evaluation value of 0.6514 loss, 95.59% accuracy, and 0.6514 precision.

Keywords

Recommendation System Content-Based Filtering Classification Semantic Fitur
Salsabil, A. A., Setiawan, E. B., & Kurniawan, I. (2024). Content-based Filtering Movie Recommender System Using Semantic Approach with Recurrent Neural Network Classification and SGD. Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, 9(2), 193-202. https://doi.org/10.22219/kinetik.v9i2.1940
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