Recommender System Based on Tweets with Singular Value Decomposition and Support Vector Machine Classification
-
Rafi Anandita Wicaksono
*
Telkom University, Bandung,
Indonesia
- Erwin Budi Setiawan Telkom University, Bandung, Indonesia (*) Corresponding Author
Abstract
In modern times, the movie industry is growing rapidly. Netflix is one of the platforms that can be used to watch movies and provides many types of genres and movie titles. With so many genres and movie titles sometimes making it difficult for people to choose a movie to watch, one solution to the problem is a recommendation system that can recommend movies based on user ratings. One method in the recommendation system is collaborative filtering. One of the algorithms contained in collaborative filtering is singular value decomposition. Twitter is one of the places where people often write their opinions about the movies they have watched, from people's tweets on Twitter will be processed into rating value data. In this system, tweets become input that is processed into data that has a rating. This research implements a user-based recommendation system based on ratings from tweets using collaborative filtering combined with the Singular Value Decomposition (SVD) algorithm and Support Vector Machine (SVM) classification and implemented it on user-based and item-based. This research aims to implement a system that combines collaborative filtering techniques with the Singular Value Decomposition (SVD) algorithm and Support Vector Machine (SVM) classification. With the hope of producing a good movie recommendation model and providing accurate predictions for recommended and non-recommended movies. The test results in this study show that Collaborative Filtering gets the best RMSE value of 0.8162 on user-based and 0.5911 on item-based. The combination of Singular Value Decomposition (SVD) algorithm and Support Vector Machine (SVM) classification using hyperparameter tuning resulted in 81% precision and 81% recall for user-based while 80% precision and 80% recall for item-based.
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References
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.
H. Tahmasebi, R. Ravanmehr, and R. Mohamadrezaei, “Social movie recommender system based on deep autoencoder network using Twitter data,” Neural Comput. Appl., vol. 33, no. 5, pp. 1607–1623, 2021, doi: 10.1007/s00521-020-05085-1.
X. Wang, Z. Dai, H. Li, and J. Yang, “A New Collaborative Filtering Recommendation Method Based on Transductive SVM and Active Learning,” Discret. Dyn. Nat. Soc., vol. 2020, no. 1, 2020, doi: 10.1155/2020/6480273.
H. D. Februariyanti, “IMPLEMENTASI METODE COLLABORATIVE FILTERING UNTUK SISTEM Diterima : Diterbitkan :,” vol. IX, no. I, pp. 43–50, 2021.
S. Zheng, C. Ding, and F. Nie, “Regularized Singular Value Decomposition and Application to Recommender System,” no. 3, 2018, [Online]. Available: http://arxiv.org/abs/1804.05090
X. Yu, Y. Chu, F. Jiang, Y. Guo, and D. Gong, “SVMs Classification Based Two-side Cross Domain Collaborative Filtering by inferring intrinsic user and item features,” Knowledge-Based Syst., vol. 141, pp. 80–91, 2018, doi: 10.1016/j.knosys.2017.11.010.
L. Ren and W. Wang, “An SVM-based collaborative filtering approach for Top-N web services recommendation,” Futur. Gener. Comput. Syst., vol. 78, pp. 531–543, 2018, doi: 10.1016/j.future.2017.07.027.
A. P. Gopi, R. N. S. Jyothi, V. L. Narayana, and K. S. Sandeep, “Classification of tweets data based on polarity using improved RBF kernel of SVM,” Int. J. Inf. Technol., 2020, doi: 10.1007/s41870-019-00409-4.
V. Subramaniyaswamy, R. Logesh, M. Chandrashekhar, A. Challa, and V. Vijayakumar, “A personalised movie recommendation system based on collaborative filtering,” Int. J. High Perform. Comput. Netw., vol. 10, no. 1–2, pp. 54–63, 2017, doi: 10.1504/IJHPCN.2017.083199.
P. Thakkar, K. Varma, V. Ukani, S. Mankad, and S. Tanwar, Collaborative Filtering Using Machine Learning. Springer Singapore, 2019. doi: 10.1007/978-981-13-1747-7.
G. Karypis, J. Konstan, and J. Riedl, “Incremental Singular Value Decomposition Algorithms for Highly Scalable Recommender Systems Dimensionality Reduction for,” Science (80-. )., vol. 81, no. 4, pp. 717–733, 2015.
N. Samaiya, S. K. Raghuwanshi, and R. K. Pateriya, Shilling attack detection in recommender system using PCA and SVM, vol. 813. Springer Singapore, 2019. doi: 10.1007/978-981-13-1498-8_55.
A. Tripathi and A. K. Sharma, “Recommending Restaurants: A Collaborative Filtering Approach,” ICRITO 2020 - IEEE 8th Int. Conf. Reliab. Infocom Technol. Optim. (Trends Futur. Dir., pp. 1165–1169, 2020, doi: 10.1109/ICRITO48877.2020.9197946.
I. Monika Parapat and M. Tanzil Furqon, “Penerapan Metode Support Vector Machine (SVM) Pada Klasifikasi Penyimpangan Tumbuh Kembang Anak,” J. Pengemb. Teknol. Inf. dan Ilmu Komput., vol. 2, no. 10, pp. 3163–3169, 2018, [Online]. Available: http://j-ptiik.ub.ac.id
A. R. D. Pratiwi and E. B. Setiawan, “Implementation of Rumor Detection on Twitter Using the SVM Classification Method,” no. 10, pp. 782–789, 2021.
G. Y. N. N. Adi, M. H. Tandio, V. Ong, and D. Suhartono, “Optimization for Automatic Personality Recognition on Twitter in Bahasa Indonesia,” Procedia Comput. Sci., vol. 135, pp. 473–480, 2018, doi: 10.1016/j.procs.2018.08.199.
Gita Safitri and Erwin Budi Setiawan, “Optimization Prediction of Big Five Personality in Twitter Users,” J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 6, no. 1, pp. 85–91, 2022, doi: 10.29207/resti.v6i1.3529.
F. Fkih, “Similarity measures for Collaborative Filtering-based Recommender Systems: Review and experimental comparison,” J. King Saud Univ. - Comput. Inf. Sci., no. xxxx, 2021, doi: 10.1016/j.jksuci.2021.09.014.
G. Xu, G. Jia, L. Shi, and Z. Zhang, “Personalized Course Recommendation System Fusing with Knowledge Graph and Collaborative Filtering,” Comput. Intell. Neurosci., vol. 2021, 2021, doi: 10.1155/2021/9590502.
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