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  3. Vol. 9, No. 2, May 2024
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Vol. 9, No. 2, May 2024

Issue Published : May 31, 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
Article Published : May 31, 2024

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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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References
  1. R. Singla, S. Gupta, A. Gupta, and D. K. Vishwakarma, “FLEX: A content based movie recommender,” 2020 Int. Conf. Emerg. Technol. INCET 2020, pp. 8–11, 2020, doi: 10.1109/INCET49848.2020.9154163. https://doi.org/10.1109/INCET49848.2020.9154163
  2. M. H. Mohamed, M. H. Khafagy, and M. H. Ibrahim, “Recommender Systems Challenges and Solutions Survey,” Proc. 2019 Int. Conf. Innov. Trends Comput. Eng. ITCE 2019, no. February, pp. 149–155, 2019 https://doi.org/10.1109/ITCE.2019.8646645
  3. T. Singh, A. Nayyar, and A. Solanki, Multilingual Opinion Mining Movie Recommendation System Using RNN, vol. 121, no. April. Springer Singapore, 2020. https://doi.org/10.1007/978-981-15-3369-3_44
  4. R. Fiagbe, “Movie Recommender System Using Matrix Factorization,” no. May, 2023.
  5. S. Reddy, S. Nalluri, S. Kunisetti, S. Ashok, and B. Venkatesh, “Content-based movie recommendation system using genre correlation,” Smart Innov. Syst. Technol., vol. 105, pp. 391–397, 2019. https://doi.org/10.1007/978-981-13-1927-3_42
  6. D. P. Kumar, A. K. Singh, S. N. Arepu, M. Sarvasuddi, E. Gowtham, and Y. Sanjana, Content Based Recommendation System on Movies. Atlantis Press International BV, 2023. https://doi.org/10.2991/978-94-6463-252-1_49
  7. J. J. Jung and D. Camacho, “Extending collaborative filtering recommendation using word embedding : A hybrid approach,” no. February, 2021, doi: 10.1002/cpe.6232. https://doi.org/10.1002/cpe.6232
  8. G. D. S. P. Moreira, S. Rabhi, J. M. Lee, R. Ak, and E. Oldridge, “Transformers4Rec: Bridging the Gap between NLP and sequential/session-based recommendation,” RecSys 2021 - 15th ACM Conf. Recomm. Syst., no. Lm, pp. 143–153, 2021, doi: 10.1145/3460231.3474255. https://doi.org/10.1145/3460231.3474255
  9. H. D. Tran, “Towards a new generation of deep learning based recommender systems Examiner ’ s Copy,” no. June, 2021. https://doi.org/10.25949/23962359.v1
  10. S. P. H, S. K. A, M. Akanksha, K. R. A, and P. R. P. P, “MOVIE RECOMMENDATION SYSTEM USING OPTIMIZED RNN APPROACH,” no. 03, pp. 2078–2081, 2023. https://www.doi.org/10.56726/IRJMETS34674
  11. S. Sahu, R. Kumar, M. S. Pathan, J. Shafi, Y. Kumar, and M. F. Ijaz, “Movie Popularity and Target Audience Prediction Using the Content-Based Recommender System,” IEEE Access, vol. 10, pp. 42030–42046, 2022, doi: 10.1109/ACCESS.2022.3168161. https://doi.org/10.1109/ACCESS.2022.3168161
  12. N. Yang, J. Jo, M. Jeon, W. Kim, and J. Kang, “Semantic and explainable research-related recommendation system based on semi-supervised methodology using BERT and LDA models,” Expert Syst. Appl., vol. 190, no. March 2021, p. 116209, 2022, doi: 10.1016/j.eswa.2021.116209. https://doi.org/10.1016/j.eswa.2021.116209
  13. C. I. M. Information, “Improving Conversational Recommendation Systems’ Quality with Context-Aware Item Meta Information,” 2020. https://doi.org/10.48550/arXiv.2112.08140
  14. K. Sarode and S. R. Javaji, “Multi-BERT for Embeddings for Recommendation System.” https://doi.org/10.48550/arXiv.2308.13050
  15. M. Chiny, M. Chihab, O. Bencharef, and Y. Chihab, “Netflix Recommendation System based on TF-IDF and Cosine Similarity Algorithms,” no. Bml 2021, pp. 15–20, 2022, doi: 10.5220/0010727500003101. http://dx.doi.org/10.5220/0010727500003101
  16. I. Nadhirah Joharee, N. Nur Wahidah Nik Hashim, and N. Syahirah Mohd Shah, “Sentiment Analysis and Text Classification for Depression Detection,” J. Integr. Adv. Eng., vol. 3, no. 1, pp. 65–78, 2023. https://doi.org/10.51662/jiae.v3i1.86
  17. S. Sridhar, D. Dhanasekaran, and G. C. P. Latha, “Content-Based Movie Recommendation System Using MBO with DBN,” Intell. Autom. Soft Comput., vol. 35, no. 3, pp. 3241–3257, 2023, doi: 10.32604/iasc.2023.030361. http://dx.doi.org/10.32604/iasc.2023.030361
  18. M. K. Delimayanti et al., “Web-Based Movie Recommendation System using Content-Based Filtering and KNN Algorithm,” Proc. - 2022 9th Int. Conf. Inf. Technol. Comput. Electr. Eng. ICITACEE 2022, no. March 2023, pp. 314–318, 2022, doi: 10.1109/ICITACEE55701.2022.9923974. https://doi.org/10.1109/ICITACEE55701.2022.9923974
  19. W. Fan et al., “Recommender Systems in the Era of Large Language Models (LLMs),”. https://doi.org/10.48550/arXiv.2307.02046.
  20. H. Du et al., Contrastive Learning with Bidirectional Transformers for Sequential Recommendation, vol. 1, no. 1. Association for Computing Machinery, 2022. https://doi.org/10.1145/3511808.3557266
  21. M. Rostami, M. Oussalah, and V. Farrahi, “A Novel Time-Aware Food Recommender-System Based on Deep Learning and Graph Clustering,” IEEE Access, vol. 10, pp. 52508–52524, 2022, doi: 10.1109/ACCESS.2022.3175317. https://doi.org/10.1109/ACCESS.2022.3175317
  22. D. S. X, W. Vossen, and R. Raymaekers, “Zero-Shot Recommendation as Language Modeling,” no. 1, pp. 1–8. https://doi.org/10.1007/978-3-030-99739-7_26
  23. H. Liu et al., “LogiQA 2.0 - An Improved Dataset for Logical Reasoning in Natural Language Understanding,” IEEE/ACM Trans. Audio Speech Lang. Process., vol. 31, pp. 2947–2962, 2023, doi: 10.1109/TASLP.2023.3293046. https://doi.org/10.1109/TASLP.2023.3293046
  24. H. Fulzele, “Movie Recommender System using Content Based and Collaborative Filtering,” vol. 8, no. 5, pp. 1009–1015, 2023.
  25. S. Jayalakshmi, N. Ganesh, R. Čep, and J. S. Murugan, “Movie Recommender Systems: Concepts, Methods, Challenges, and Future Directions,” Sensors, vol. 22, no. 13, 2022, doi: 10.3390/s22134904. https://doi.org/10.3390/s22134904
  26. M. Khoali, A. Tali, and Y. Laaziz, “Advanced Recommendation Systems Through Deep Learning,” ACM Int. Conf. Proceeding Ser., no. March, 2020, doi: 10.1145/3386723.3387870. https://doi.org/10.1145/3386723.3387870
  27. I. M. Al Jawarneh et al., “A Pre-Filtering Approach for Incorporating Contextual Information into Deep Learning Based Recommender Systems,” IEEE Access, vol. 8, pp. 40485–40498, 2020, doi: 10.1109/ACCESS.2020.2975167. https://doi.org/10.1109/ACCESS.2020.2975167
  28. G. S. Rao, G. V. Kumari, and B. P. Rao, “Network for Biomedical Applications,” vol. 2, no. January, pp. 107–119, 2019, doi: 10.1007/978-981-13-1595-4. https://doi.org/10.1007/978-981-13-1595-4_12
  29. A. Usman, A. Roko, A. B. Muhammad, and A. Almu, “Enhancing Personalized Book Recommender System,” Int. J. Adv. Netw. Appl., vol. 14, no. 03, pp. 5486–5492, 2022. https://doi.org/10.35444/ijana.2022.14311
  30. Z. Romadhon, E. Sediyono, and C. E. Widodo, “Various Implementation of Collaborative Filtering-Based Approach on Recommendation Systems using Similarity,” Kinet. Game Technol. Inf. Syst. Comput. Network, Comput. Electron. Control, vol. 4, no. 3, pp. 179–186, 2020, doi: 10.22219/kinetik.v5i3.1062. https://doi.org/10.22219/kinetik.v5i3.1062
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References


R. Singla, S. Gupta, A. Gupta, and D. K. Vishwakarma, “FLEX: A content based movie recommender,” 2020 Int. Conf. Emerg. Technol. INCET 2020, pp. 8–11, 2020, doi: 10.1109/INCET49848.2020.9154163. https://doi.org/10.1109/INCET49848.2020.9154163

M. H. Mohamed, M. H. Khafagy, and M. H. Ibrahim, “Recommender Systems Challenges and Solutions Survey,” Proc. 2019 Int. Conf. Innov. Trends Comput. Eng. ITCE 2019, no. February, pp. 149–155, 2019 https://doi.org/10.1109/ITCE.2019.8646645

T. Singh, A. Nayyar, and A. Solanki, Multilingual Opinion Mining Movie Recommendation System Using RNN, vol. 121, no. April. Springer Singapore, 2020. https://doi.org/10.1007/978-981-15-3369-3_44

R. Fiagbe, “Movie Recommender System Using Matrix Factorization,” no. May, 2023.

S. Reddy, S. Nalluri, S. Kunisetti, S. Ashok, and B. Venkatesh, “Content-based movie recommendation system using genre correlation,” Smart Innov. Syst. Technol., vol. 105, pp. 391–397, 2019. https://doi.org/10.1007/978-981-13-1927-3_42

D. P. Kumar, A. K. Singh, S. N. Arepu, M. Sarvasuddi, E. Gowtham, and Y. Sanjana, Content Based Recommendation System on Movies. Atlantis Press International BV, 2023. https://doi.org/10.2991/978-94-6463-252-1_49

J. J. Jung and D. Camacho, “Extending collaborative filtering recommendation using word embedding : A hybrid approach,” no. February, 2021, doi: 10.1002/cpe.6232. https://doi.org/10.1002/cpe.6232

G. D. S. P. Moreira, S. Rabhi, J. M. Lee, R. Ak, and E. Oldridge, “Transformers4Rec: Bridging the Gap between NLP and sequential/session-based recommendation,” RecSys 2021 - 15th ACM Conf. Recomm. Syst., no. Lm, pp. 143–153, 2021, doi: 10.1145/3460231.3474255. https://doi.org/10.1145/3460231.3474255

H. D. Tran, “Towards a new generation of deep learning based recommender systems Examiner ’ s Copy,” no. June, 2021. https://doi.org/10.25949/23962359.v1

S. P. H, S. K. A, M. Akanksha, K. R. A, and P. R. P. P, “MOVIE RECOMMENDATION SYSTEM USING OPTIMIZED RNN APPROACH,” no. 03, pp. 2078–2081, 2023. https://www.doi.org/10.56726/IRJMETS34674

S. Sahu, R. Kumar, M. S. Pathan, J. Shafi, Y. Kumar, and M. F. Ijaz, “Movie Popularity and Target Audience Prediction Using the Content-Based Recommender System,” IEEE Access, vol. 10, pp. 42030–42046, 2022, doi: 10.1109/ACCESS.2022.3168161. https://doi.org/10.1109/ACCESS.2022.3168161

N. Yang, J. Jo, M. Jeon, W. Kim, and J. Kang, “Semantic and explainable research-related recommendation system based on semi-supervised methodology using BERT and LDA models,” Expert Syst. Appl., vol. 190, no. March 2021, p. 116209, 2022, doi: 10.1016/j.eswa.2021.116209. https://doi.org/10.1016/j.eswa.2021.116209

C. I. M. Information, “Improving Conversational Recommendation Systems’ Quality with Context-Aware Item Meta Information,” 2020. https://doi.org/10.48550/arXiv.2112.08140

K. Sarode and S. R. Javaji, “Multi-BERT for Embeddings for Recommendation System.” https://doi.org/10.48550/arXiv.2308.13050

M. Chiny, M. Chihab, O. Bencharef, and Y. Chihab, “Netflix Recommendation System based on TF-IDF and Cosine Similarity Algorithms,” no. Bml 2021, pp. 15–20, 2022, doi: 10.5220/0010727500003101. http://dx.doi.org/10.5220/0010727500003101

I. Nadhirah Joharee, N. Nur Wahidah Nik Hashim, and N. Syahirah Mohd Shah, “Sentiment Analysis and Text Classification for Depression Detection,” J. Integr. Adv. Eng., vol. 3, no. 1, pp. 65–78, 2023. https://doi.org/10.51662/jiae.v3i1.86

S. Sridhar, D. Dhanasekaran, and G. C. P. Latha, “Content-Based Movie Recommendation System Using MBO with DBN,” Intell. Autom. Soft Comput., vol. 35, no. 3, pp. 3241–3257, 2023, doi: 10.32604/iasc.2023.030361. http://dx.doi.org/10.32604/iasc.2023.030361

M. K. Delimayanti et al., “Web-Based Movie Recommendation System using Content-Based Filtering and KNN Algorithm,” Proc. - 2022 9th Int. Conf. Inf. Technol. Comput. Electr. Eng. ICITACEE 2022, no. March 2023, pp. 314–318, 2022, doi: 10.1109/ICITACEE55701.2022.9923974. https://doi.org/10.1109/ICITACEE55701.2022.9923974

W. Fan et al., “Recommender Systems in the Era of Large Language Models (LLMs),”. https://doi.org/10.48550/arXiv.2307.02046.

H. Du et al., Contrastive Learning with Bidirectional Transformers for Sequential Recommendation, vol. 1, no. 1. Association for Computing Machinery, 2022. https://doi.org/10.1145/3511808.3557266

M. Rostami, M. Oussalah, and V. Farrahi, “A Novel Time-Aware Food Recommender-System Based on Deep Learning and Graph Clustering,” IEEE Access, vol. 10, pp. 52508–52524, 2022, doi: 10.1109/ACCESS.2022.3175317. https://doi.org/10.1109/ACCESS.2022.3175317

D. S. X, W. Vossen, and R. Raymaekers, “Zero-Shot Recommendation as Language Modeling,” no. 1, pp. 1–8. https://doi.org/10.1007/978-3-030-99739-7_26

H. Liu et al., “LogiQA 2.0 - An Improved Dataset for Logical Reasoning in Natural Language Understanding,” IEEE/ACM Trans. Audio Speech Lang. Process., vol. 31, pp. 2947–2962, 2023, doi: 10.1109/TASLP.2023.3293046. https://doi.org/10.1109/TASLP.2023.3293046

H. Fulzele, “Movie Recommender System using Content Based and Collaborative Filtering,” vol. 8, no. 5, pp. 1009–1015, 2023.

S. Jayalakshmi, N. Ganesh, R. Čep, and J. S. Murugan, “Movie Recommender Systems: Concepts, Methods, Challenges, and Future Directions,” Sensors, vol. 22, no. 13, 2022, doi: 10.3390/s22134904. https://doi.org/10.3390/s22134904

M. Khoali, A. Tali, and Y. Laaziz, “Advanced Recommendation Systems Through Deep Learning,” ACM Int. Conf. Proceeding Ser., no. March, 2020, doi: 10.1145/3386723.3387870. https://doi.org/10.1145/3386723.3387870

I. M. Al Jawarneh et al., “A Pre-Filtering Approach for Incorporating Contextual Information into Deep Learning Based Recommender Systems,” IEEE Access, vol. 8, pp. 40485–40498, 2020, doi: 10.1109/ACCESS.2020.2975167. https://doi.org/10.1109/ACCESS.2020.2975167

G. S. Rao, G. V. Kumari, and B. P. Rao, “Network for Biomedical Applications,” vol. 2, no. January, pp. 107–119, 2019, doi: 10.1007/978-981-13-1595-4. https://doi.org/10.1007/978-981-13-1595-4_12

A. Usman, A. Roko, A. B. Muhammad, and A. Almu, “Enhancing Personalized Book Recommender System,” Int. J. Adv. Netw. Appl., vol. 14, no. 03, pp. 5486–5492, 2022. https://doi.org/10.35444/ijana.2022.14311

Z. Romadhon, E. Sediyono, and C. E. Widodo, “Various Implementation of Collaborative Filtering-Based Approach on Recommendation Systems using Similarity,” Kinet. Game Technol. Inf. Syst. Comput. Network, Comput. Electron. Control, vol. 4, no. 3, pp. 179–186, 2020, doi: 10.22219/kinetik.v5i3.1062. https://doi.org/10.22219/kinetik.v5i3.1062

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KINETIK: Game Technology, Information System, Computer Network, Computing, Electronics, and Control
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