Movie Recommendation Using K-Nearest Neighbors Algorithm

Nagireddy Varshitha Reddy; Praveen S R; Peddireddy Usha Sri Reddy; Polu Supra Geethika; Nuthanapati Meghana

doi:10.32628/IJSRCSEIT

Authors

Nagireddy Varshitha Reddy U G Student, Department of Computer Science & Engineering, R. L. Jalappa Institute of Technology, Doddaballapur, Karnataka, India Author
Praveen S R Assistant Professor, Department of Computer Science Engineering, R L Jalappa Institute of Technology, Doddaballapur – 561203, Karnataka, India Author
Peddireddy Usha Sri Reddy U G Student, Department of Computer Science & Engineering, R. L. Jalappa Institute of Technology, Doddaballapur, Karnataka, India Author
Polu Supra Geethika U G Student, Department of Computer Science & Engineering, R. L. Jalappa Institute of Technology, Doddaballapur, Karnataka, India Author
Nuthanapati Meghana U G Student, Department of Computer Science & Engineering, R. L. Jalappa Institute of Technology, Doddaballapur, Karnataka, India Author

DOI:

https://doi.org/10.32628/IJSRCSEIT

Keywords:

Machine Learning, K-Nearest Neighbors Algorithm, Implementation

Abstract

In a landscape brimming with an extensive array of movies globally, navigating through the multitude of options to find films that suit one's unique preferences can prove to be a difficult assignment for viewers. The sheer volume of choices often leaves individuals feeling overwhelmed, presenting a challenge in selecting a movie that resonates with their tastes. Consequently, movie Service providers are accountable for the duty of furnishing a recommendation system that enhances the user experience by assisting them in discovering movies that complement their preferences. Previous research into recommendation systems, particularly those employing Machine Learning (ML) algorithms, has proved they were better than the conventional recommendation methods. However, there remains a requirement for further refinement, especially in circumstances in which users face difficulties in identifying movies within their favorite genres. Prolonged looks for the appropriate film can exacerbate issues such as information scarcity and chilly launch challenges. To deal with these problems effectively, we propose the implementation of a recommender system based on machine learning focused on movie genres, leveraging the algorithm known as KNN, or K-nearest Neighbors. Our proposed solution features a user-friendly interface hosted in a well-lit web application, equipped utilizing a slider bar functionality that empowers users to specify their taste in movies and get personalized suggestions for similar titles. Through the integration of user feedback and choices, our system aims to deliver customized advice that is better suited to individual interests and tastes, thereby improving the overall movie-viewing experience.

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References

Mohiuddin A, Abdun NM, Jiankun H. Outlier detec- tion. In The State of the Art in Intrusion Prevention and Detection, Al-Sakib Khan Pathan (ed). Chapter: 1, Publisher: CRC Press: New York, USA, 2014. DOI: 10.1201/b16390-3

J. Kumaraswamy, Anil K C, T R Veena, G. Purushotham, Sunil Kumar K, “Investigating the Mechanical Properties of Al 7075 Alloy for Automotive Applications: Synthesis and Analysis” in Scopus indexed EVERGREEN Journal with ISSN: 2189-0420, Vol. 10, Issue 03, pp.1286-1295, September 2023.

Bilge L, Balzarotti D, Robertson W, Kirda E, Kruegel C. Disclosure: detecting botnet command and control servers through large-scale NetFlow analysis. Proceed- ings of the 28th Annual Computer Security Applica- tions Conference. 2012, 129–138

Kumaraswamy, J., Anil, K.C., Canbay, C.A., N D Shiva Kumar. Electro-Whirling Stir Casting: a Novel Approach for Fabricating Al7075/SiC MMCs with Enhanced Thermal Characteristics. Silicon. https://doi.org/10.1007/s12633-023-02678-y

Münz G, Li S, Carle G. Traffic anomaly detection using k-means clustering. In Proceedings of Performance, Reliability and Dependability Evaluation of Communi- cation Networks and Distributed Systems, 4 GI / ITG Workshop MMBnet. Hamburg, Germany. 2007

Kumaraswamy Jayappa, Kyathasandra Chikkanna Anil, Zulfiqar A. Khan, Enhancing wear resistance in Al-7075 composites through conventional mixing and casting techniques, Journal of Materials Research and Technology, Volume 27, 2023, pp. 7935-7945. https://doi.org/10.1016/j.jmrt.2023.11.171.

Hofstede R, Bartos V, Sperotto A, Pras A. Towards real-time intrusion detection for NetFlow and IPFIX. In: 9th International Conference on Network and Ser- vice Management, CNSM 2013, October 2013, Zürich, Switzerland. 2013, 14–18

Kumaraswamy, J., Anil, K. C., Veena, T. R., Reddy, M., & Sunil Kumar, K. (2023). Influence of particulates on microstructure, Mechanical and Fractured behaviour on Al-7075 alloy composite by FEA. Australian Journal of Mechanical Engineering, 1–15. https://doi.org/10.1080/14484846.2023.2276987

Lazarevic A, Ertoz L, Kumar V, Ozgur A, Srivastava J. A comparative study of anomaly detection schemes in network intrusion detection. In Proceedings of the Third SIAM International Conference on Data Mining. 2003

J. Kumaraswamy et al., "Thermal Analysis of Ni-Cu Alloy Nanocomposites Processed by Sand Mold Casting," Advances in Materials Science and Engineering, vol. 2022, Article ID 2530707, 11 pages, 2022. https://doi.org/10.1155/2022/2530707.

Gogoi P, Bhattacharyya DK, Borah B, Kalita JK. A survey of outlier detection methods in network anom- aly identification. The Computer Journal 2011; 54(4):570–588.

J. Kumaraswamy, K.C. Anil and V. Shetty, Development of Ni-Cu based alloy hybrid composites through induction furnace casting, Materials Today: Proceedings. https://doi.org/10.1016/j.matpr.2022.09.215

Chandola V, Banerjee A, Kumar V. Anomaly detec- tion: a survey. ACM Computing Surveys (CSUR) 2009; 41(3):15–58.

Anil Kyathasandra Chikkanna, Kuchangi Venkatappa Manjunath, Kumaraswamy Jayappa, Mahadeva Reddy, Akash Biradar, Effect of Chilling & B4C content on Machining Efficiency and Surface Quality in Wire-Cut Machining of Aluminum Matrix Chilled Composites, Mechanics of Advanced Composite Structures, Volume 11, Issue 2 Pages 341-350. https://doi.org/10.22075/macs.2024.31090.1528

Breunig MM, Kriegel HP, Ng RT, Sander J. LOF: identifying density-based local outliers. In Proceedings of the ACM SIGMOD International Conference on Management of Data, Dallas, TX. 2000, 93–104.

Garšva E, Paulauskas N, Gražulevičius G, Gulbinovič L. Packet inter-arrival time distribution in academic computer network. Elektronika ir elektrotechnika. Elec- tronics and Electrical Engineering 2014; 20(3):87–90.