Theoretical and Natural Science

Theoretical and Natural Science

TNS Vol.2 (CIAP 2022), 02 February 2023


Open Access | Article

A Content-based Movie Recommendation System

Yiting Yuan * 1 , Youyang Qin 2 , Zekai Yu 3 , Congbai Zhang 4
1 Yiting Yuan, Department of business, Rutgers University, Camden, NJ, 08102, United States
2 Youyang Qin, Abington Friends School, Jenkintown, PA, 19046, United States
3 Zekai Yu, Department of Statistics and Data Science, University of Washington, Seattle, 98105, United States
4 Congbai Zhang, Barry Florescue Undergraduate Business Program, University of Rochester, Rochester, NY, 14627, United States

* Author to whom correspondence should be addressed.

Theoretical and Natural Science, TNS Vol.2 (CIAP 2022), 161-171
Published 02 February 2023. © 2023 The Author(s). Published by EWA Publishing
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Citation Yiting Yuan, Youyang Qin, Zekai Yu, Congbai Zhang. A Content-based Movie Recommendation System. TNS (2023) TNS Vol.2 (CIAP 2022): 161-171.

Abstract

In this paper, we describe a content-based movie recommendation system and provide an overview of the movie recommendation systems in today's market. Our findings show 1): Summary-Based and Feature-Based movie recommendation systems will provide different recommendation results. 2) Combined recommendation system’s result is consistent with the Summary-Based recommendation system but different from the Feature-Based recommendation system. Based on our recommendation system, we also made some innovations and fusion and conducted several control tests to improve the quality of our recommendations.

References

1. Jena, A. (2022, March 17). Role of a movie recommender system in the streaming industry. Muvi One. Retrieved July 8, 2022, from https://www.muvi.com/blogs/movie-recommender-system.html

2. Sharma, L., & Gera, A. (n.d.). A Survey of Recommendation System: Research Challenges. Redirecting. Retrieved July 8, 2022, from https://answers.microsoft.com/en-us/windows/forum/all/cusersusernamedocumentsfile-folder-name/3c43589c-b582-433b-99ea-cfe3e1b2a270

3. Ahmed, M. (n.d.). Movie recommendation system using clustering and Pattern Recognition Network. IEEE Xplore. Retrieved July 8, 2022, from https://ieeexplore.ieee.org/document/8301695/

4. Uluyagmur, M. (n.d.). Content-based movie recommendation using different feature sets. Retrieved July 8, 2022, from http://www.iaeng.org/publication/WCECS2012/WCECS2012_pp517-521.pdf

5. Lops, P., Jannach, D., Musto, C., Bogers, T., & Koolen, M. (2019, March 7). Trends in content-based recommendation - user modeling and user-adapted interaction. SpringerLink. Retrieved July 8, 2022, from https://link.springer.com/article/10.1007/s11257-019-09231-w

6. Singh, R. H. (n.d.). Movie recommendation system using cosine similarity and KNN. Retrieved July 8, 2022, from https://www.researchgate.net/publication/344627182_Movie_Recommendation_System_using_Cosine_Similarity_and_KNN

7. Tewari, A. S., Singh, J. P., & Barman, A. G. (2018, June 8). Generating top-N items recommendation set using collaborative, content based filtering and rating variance. Procedia Computer Science. Retrieved July 1, 2022, from https://www.sciencedirect.com/science/article/pii/S1877050918308718

8. Wu, C.-S. M. (n.d.). Movie recommendation system using collaborative filtering. IEEE Xplore. Retrieved July 8, 2022, from https://ieeexplore.ieee.org/abstract/document/8663822

9. Keshava, M. C., Srinivasulu, S., Reddy, P. N., & Naik, B. D. (2020). Machine learning model for movie recommendation system. International Journal of Engineering Research & Technology (IJERT), 9(04).

10. Uddin, M. N. (n.d.). Enhanced content-based filtering using diverse collaborative prediction for movie recommendation. IEEE Xplore. Retrieved July 8, 2022, from https://ieeexplore.ieee.org/document/5175981

11. Afoudi, Y., Lazaar, M., & Achhab, M. A. (2021, July 24). Hybrid recommendation system combined content-based filtering and collaborative prediction using Artificial Neural Network. Simulation Modelling Practice and Theory. Retrieved July 1, 2022, from https://www.sciencedirect.com/science/article/pii/S1569190X21000836

12. Mubarak, S. (2021, August 18). Netflix dataset latest 2021. Kaggle. Retrieved July 8, 2022, from https://www.kaggle.com/datasets/syedmubarak/netflix-dataset-latest-202

13. Heidenreich, H. (2018, August 16). Introduction to word embeddings. Medium. Retrieved July 8, 2022, from https://towardsdatascience.com/introduction-to-word-embeddings-4cf857b12edc

14. Saket, S. (2020, January 12). Count vectorizers vs TFIDF vectorizers: Natural language processing. Medium. Retrieved July 8, 2022, from https://medium.com/artificial-coder/count-vectorizers-vs-tfidf-vectorizers-natural-language-processing-b5371f51a40c

15. Ahmed, I. (2020, May 16). Getting started with a movie recommendation system. Kaggle. Retrieved July 8, 2022, from https://www.kaggle.com/code/ibtesama/getting-started-with-a-movie-recommendation-system

16. Han, J., & Pei, J. (n.d.). Cosine similarity. Cosine Similarity - an overview | ScienceDirect Topics. Retrieved July 8, 2022, from https://www.sciencedirect.com/topics/computer-science/cosine-similarity

17. Dangeti, P. (n.d.). Statistics for Machine Learning. O'Reilly Online Learning. Retrieved July 8, 2022, from https://www.oreilly.com/library/view/statistics-for-machine/9781788295758/eb9cd609-e44a-40a2-9c3a-f16fc4f5289a.xhtml

18. Singh, Ramni & Maurya, Sargam & Tripathi, Tanisha & Narula, Tushar & Srivastav, Gaurav. (2020). Movie Recommendation System using Cosine Similarity and KNN. International Journal of Engineering and Advanced Technology. 9. 2249-8958. 10.35940/ijeat.E9666.069520.

Data Availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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