Weight-Based Hybrid Filtering in a Movie Recommendation System Based on Twitter with LSTM Classification

Muhammad Nur Ilyas, Erwin Budi Setiawan

Abstract


With the era of digitalization, movie-watching has gained immense popularity, with platforms like Disney+ offering easy access to a variety of films. After watching, users frequently share their opinions on social media platforms such as Twitter, because of it is freedom of expression. With numerous movies available, users frequently encounter challenges in deciding what to watch. To address this, a recommendation system is proposed to streamline the decision-making process for users. Collaborative Filtering (CF), Content-Based Filtering (CBF), and Hybrid Filtering are common techniques used in recommendation systems. However, CF and CBF techniques face issues like cold start, sparse data, and overspecialization. To overcome these, this research constructs a Hybrid Filtering recommendation system, with a weight-based of CF-CBF coupled with Long Short-Term Memory (LSTM) classification. The classification uses various optimizers, including Adam, SGD, Nadam, RMSprop, and Adamax. Dataset is sourced from Kaggle website, which includes movie-related tweets linked to the Disney+ platform. The results indicate that Weight-Based Hybrid Filtering utilizing Adamax optimizer in LSTM classification yields superior performance metrics, by having 78% Precision, 79% Recall, 79% Accuracy, and 77% F1-Score value.


Keywords


Disney+; Recommendation System; Collaborative Filtering; Content-Based Filtering; Hybrid Filtering; Long Short-Term Memory

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References


D. Das, H. T. Chidananda, and L. Sahoo, “Personalized Movie Recommendation System Using Twitter Data,†Advances in Intelligent Systems and Computing, vol. 710, pp. 339–347, Apr. 2018, doi: 10.1007/978-981-10-7871-2_33/COVER.

C. T. Havard, “Disney, Netflix, and Amazon Oh My! An Analysis of Streaming Brand Competition and the Impact on the Future of Consumer Entertainment,†Findings in Sport, Hospitality, Entertainment, and Event Management, vol. 1, no. 1, pp. 38–45, 2021.

R. Henriques and L. Pinto, “A novel evaluation framework for recommender systems in big data environments,†Expert Syst Appl, vol. 231, Nov. 2023, doi: 10.1016/j.eswa.2023.120659.

R. Shen, “A Recommender System Integrating Long Short-Term Memory and Latent Factor,†Arab J Sci Eng, vol. 47, no. 8, pp. 9931–9941, Jan. 2022, doi: 10.1007/s13369-021-05933-9.

M. Hussien Mohamed, M. Helmy Khafagy, and M. Hasan Ibrahim, “Recommender Systems Challenges and Solutions Survey,†2019 International Conference on Innovative Trends in Computer Engineering (ITCE), pp. 149–155, 2019, doi: 10.1109/ITCE.2019.8646645.

K. L. Cheung, D. Durusu, X. Sui, and H. de Vries, “How recommender systems could support and enhance computer-tailored digital health programs: A scoping review,†Digit Health, vol. 5, Jan. 2019, doi: 10.1177/2055207618824727.

L. Ambarwati and Z. Baizal, “Group Recommender System Using Hybrid Filtering for Tourism Domain,†Journal on Computing, vol. 4, no. 2, pp. 21–30, Sep. 2019, doi: 10.21108/indojc.2019.4.2.258.

M. Abdel-Nasser and K. Mahmoud, “Accurate photovoltaic power forecasting models using deep LSTM-RNN,†Neural Comput Appl, vol. 31, no. 7, pp. 2727–2740, Jul. 2019, doi: 10.1007/s00521-017-3225-z.

H. Zarzour, Y. Jararweh, M. M. Hammad, and M. Al-Smadi, “A long short-term memory deep learning framework for explainable recommendation,†2020 11th International Conference on Information and Communication Systems, ICICS 2020, pp. 233–237, Apr. 2020, doi: 10.1109/ICICS49469.2020.239553.

G. Geetha, M. Safa, C. Fancy, and D. Saranya, “A Hybrid Approach using Collaborative filtering and Content based Filtering for Recommender System,†J Phys Conf Ser, vol. 1000, no. 1, Apr. 2018, doi: 10.1088/1742-6596/1000/1/012101.

H. Hidayat Arfisko and A. Toto Wibowo, “Sistem Rekomendasi Film Menggunakan Metode Hybrid Collaborative Filtering Dan Content-based Filtering,†e-Proceeding of Engineering, vol. 9, pp. 2149–2159, Jun. 2022.

M. Jerónimo, F. C. Pinto, and R. P. Duarte, “Weight-Based Dynamic Hybrid Recommendation System for Web Application Content,†Proceedings of Seventh International Congress on Information and Communication Technology, vol. 464, pp. 9–17, 2023, doi: https://doi.org/10.1007/978-981-19-2394-4_2.

A. B. Chopra and V. S. Dixit, “An adaptive RNN algorithm to detect shilling attacks for online products in hybrid recommender system,†Journal of Intelligent Systems, vol. 31, no. 1, pp. 1133–1149, Oct. 2022, doi: 10.1515/jisys-2022-1023.

S. T. T. Nguyen and B. D. Tran, “Long Short-Term Memory Based Movie Recommendation,†Science & Technology Development Journal - Engineering and Technology, vol. 3, no. SI1, pp. SI1–SI9, Sep. 2020, doi: 10.32508/stdjet.v3isi1.540.

G. Liu and X. Wu, “Using Collaborative Filtering Algorithms Combined with Doc2Vec for Movie Recommendation,†2019 IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC), pp. 1461–1464, 2019, doi: 10.1109/ITNEC.2019.8729076.

G. Ramadhan and E. Budi Setiawan, “Collaborative Filtering Recommender System Based on Memory Based in Twitter Using Decision Tree Learning Classification (Case Study: Movie on Netflix),†2022 International Conference on Advanced Creative Networks and Intelligent Systems (ICACNIS), 2022, doi: 10.1109/ICACNIS57039.2022.10055248.

A. A. Fakhri, Z. K. A. Baizal, and E. B. Setiawan, “Restaurant Recommender System Using User-Based Collaborative Filtering Approach: A Case Study at Bandung Raya Region,†J Phys Conf Ser, vol. 1192, no. 1, p. 12023, Mar. 2019, doi: 10.1088/1742-6596/1192/1/012023.

M. J. Lavin, “Analyzing Documents with TF-IDF,†Programming Historian, no. 8, May 2019, doi: 10.46430/phen0082.

W. Jia, L. Chao, C. Wei, and Z. Yuxiao, “Personalized Collaborative Filtering Recommendation Algorithm based on Linear Regression,†2019 IEEE International Conference on Power Data Science (ICPDS), pp. 139–142, 2019, doi: 10.1109/ICPDS47662.2019.9017166.

B. Walek and V. Fojtik, “A hybrid recommender system for recommending relevant movies using an expert system,†Expert Syst Appl, vol. 158, Nov. 2020, doi: 10.1016/j.eswa.2020.113452.

A. Farzad, H. Mashayekhi, and H. Hassanpour, “A comparative performance analysis of different activation functions in LSTM networks for classification,†Neural Comput Appl, vol. 31, no. 7, pp. 2507–2521, Jul. 2019, doi: 10.1007/s00521-017-3210-6.

N. S. Kiruthika and D. G. Thailambal, “Dynamic Light Weight Recommendation System for Social Networking Analysis Using a Hybrid LSTM-SVM Classifier Algorithm,†Optical Memory and Neural Networks (Information Optics), vol. 31, no. 1, pp. 59–75, Mar. 2022, doi: 10.3103/S1060992X2201009X.

A. A. I. A. Maharani, S. S. Prasetiyowati, and Y. Sibaroni, “Classification of Public Sentiment on Fuel Price Increases Using CNN,†Sinkron : Jurnal Dan Penelitian Teknik Informatika, vol. 8, no. 3, Jul. 2023, doi: 10.33395/sinkron.v8i3.12609.

I. W. Mustika, H. N. Adi, and F. Najib, “Comparison of Keras Optimizers for Earthquake Signal Classification Based on Deep Neural Networks,†2021 4th International Conference on Information and Communications Technology (ICOIACT), pp. 304–308, 2021, doi: 10.1109/ICOIACT53268.2021.9563990.




DOI: https://doi.org/10.30865/mib.v7i4.6668

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