Movie Recommendation System Based on Synopsis Using Content-Based Filtering with TF-IDF and Cosine Similarity

Authors

  • Armadhani Hiro Juni Permana Juni Permana Telkom University
  • Agung Toto Wibowo

DOI:

https://doi.org/10.21108/ijoict.v9i2.747

Keywords:

Recommendation System, Content-Based Filtering, TF-IDF, Cosine Similarity

Abstract

Recommendation systems have become an interesting topic in the field of artificial intelligence and data analysis. In the current era of technological advancement, the entertainment industry is rapidly growing, particularly the film industry, which is highly popular among the public due to their enthusiasm for watching movies. The increasing number and variety of films with various genres and titles have made it challenging for users to choose a film. To assist them in selecting movies, the presence of a recommendation system is necessary to provide information or film recommendations based on user interests and preferences. In this research, the development of the recommendation system will utilize the content-based filtering method, employing the TF-IDF algorithm and cosine similarity. The dataset used in this study is derived from publicly available data (MovieLens). The results of this research demonstrate that the TF-IDF and cosine similarity algorithms provide recommendations that align with the viewers' interests, as measured by precision, recall, and f1-score calculations.

Downloads

Download data is not yet available.

References

[1] Ahmed, Muyeed, Mir Tahsin Imtiaz, and Raiyan Khan. "Movie recommendation system using clustering and pattern recognition network." 2018 IEEE 8th Annual Computing and Communication Workshop and Conference (CCWC). IEEE, 2018.
[2] Halder, Sajal, AM Jehad Sarkar, and Young-Koo Lee. "Movie recommendation system based on movie swarm." 2012 Second international conference on cloud and green computing. IEEE, 2012.
[3] Reddy, S. R. S., et al. "Content-based movie recommendation system using genre correlation." Smart Intelligent Computing and Applications. Springer, Singapore, 2019. 391-397.
[4] Sappadla, Prateek, Yash Sadhwani, and Pranit Arora. "Movie Recommender System." Search Engine Architecture, Spring (2017).
[5] Singh, Ramni Harbir, et al. "Movie recommendation system using cosine similarity and KNN." International Journal of Engineering and Advanced Technology 9.5 (2020): 556-559.
[6] Bhowmick, Hrisav, Ananda Chatterjee, and Jaydip Sen. "Comprehensive Movie Recommendation System." arXiv preprint arXiv:2112.12463 (2021).
[7] Yadalam, Tanya V., et al. "Career recommendation systems using content based filtering." 2020 5th International Conference on Communication and Electronics Systems (ICCES). IEEE, 2020.
[8] Chiny, Mohamed, et al. "Netflix Recommendation System based on TF-IDF and Cosine Similarity Algorithms." no. Bml (2022): 15-20.
[9] Alewiwi, Mahmoud, Cengiz Orencik, and Erkay SavaÅŸ. "Efficient top-k similarity document search utilizing distributed file systems and cosine similarity." Cluster Computing 19.1 (2016): 109-126.
[10] Wang, Zihuan, et al. "A news-topic recommender system based on keywords extraction." Multimedia Tools and Applications 77.4 (2018): 4339-4353.
[11] Liao, Chia-Hung, et al. "A photo post recommendation system based on topic model for improving facebook fan page engagement." Symmetry 12.7 (2020): 1105.
[12] Wiputra, Michael Mahendra, and Yusup Jauhari Shandi. "Perancangan Sistem Rekomendasi Menggunakan Metode Collaborative Filtering dengan Studi Kasus Perancangan Website Rekomendasi Film." Media Informatika 20.1 (2021): 1-18.
[13] Dzumiroh, Lisniati. "Penerapan Metode Collaborative Filtering Menggunakan Rating Implisit pada Sistem Perekomendasi Pemilihan Film di Rental VCD." (2012).
[14] Larasati, Fatoni Batari Agung, and Herny Februariyanti. "Sistem Rekomendasi Product Emina Cosmetics Dengan Menggunakan Metode Content-Based Filtering." Jurnal Manajemen Informatika Dan Sistem Informasi 4.1 (2021): 45-54.
[15] Thorat, Poonam B., Rajeshwari M. Goudar, and Sunita Barve. "Survey on collaborative filtering, content-based filtering and hybrid recommendation system." International Journal of Computer Applications 110.4 (2015): 31-36.
[16] Xiong, Wei, Simon Xu, and Wenqian Shang. "17th IEEE/ACIS International Conference on Computer and Information Science (ICIS 2018)." (2018).
[17] Gupta, Meenu, et al. "Movie recommender system using collaborative filtering." 2020 International Conference on Electronics and Sustainable Communication Systems (ICESC). IEEE, 2020.
[18] A. Yazdinejad, B. Zolfaghari, A. Dehghantanha, H. Karimipour, G. Srivastava, and R. M. Parizi, “Accurate threat hunting in industrial internet of things edge devices,” Digital Communications and Networks, Sep. 2022, doi: 10.1016/j.dcan.2022.09.010.

Downloads

Published

2023-12-08

How to Cite

Juni Permana, A. H. J. P., & Agung Toto Wibowo. (2023). Movie Recommendation System Based on Synopsis Using Content-Based Filtering with TF-IDF and Cosine Similarity. International Journal on Information and Communication Technology (IJoICT), 9(2), 1–14. https://doi.org/10.21108/ijoict.v9i2.747

Issue

Vol. 9 No. 2 (2023): Vol.9 No. 2 Dec 2023

Section

Intelligence System

License

Manuscript submitted to IJoICT has to be an original work of the author(s), contains no element of plagiarism, and has never been published or is not being considered for publication in other journals. Author(s) shall agree to assign all copyright of published article to IJoICT. Requests related to future re-use and re-publication of major or substantial parts of the article must be consulted with the editors of IJoICT.