Movie Recommendation Using Clustering and Nearest Neighbour

##plugins.themes.academic_pro.article.sidebar##

Published Oct 31, 2022
https://doi.org/10.47164/ijngc.v13i3.845
Download
Requires Subscription PDF
Statistic

Downloads

Download data is not yet available.

Metrics

Metrics Loading ...
Volume 13, Special Issue 3, October 2022

##plugins.themes.academic_pro.article.main##

Rupali Bagate
Army Institute of Technology
https://orcid.org/0000-0002-7538-5414
Aparna Joshi
Army Institute of Technology
https://orcid.org/0000-0003-3397-148X
Shilpa Pawar
Army Institute of Technology
https://orcid.org/0000-0002-8606-8430
Yogita Hambir
Army Institute of Technology
https://orcid.org/0000-0002-1459-2815
Sharayu Lokhande
Army Institute of Technology
https://orcid.org/0000-0002-7519-0588

Abstract

Due to the abundance of items available and online information, a user cannot easily choose which product is ideal for him. A recommender system assists users in finding what is best for them. A recommender system uses information about a user's activity. It uses it to suggest movies to users based on their individual interests. This paper provides an overview of a recommender system that uses the K-means and KNN algorithm. Without wasting time exploring, both algorithms rapidly and effectively recommend movies to customers based on their likes. There are many uses for recommender systems worldwide. K-means algorithm is used to get beyond some of the restrictions of content-based and collaborative work. The K-means algorithm creates clusters of individuals with similar interests, and KNN, which includes nearest neighbors, recommends movies to each group. This is used in well-known fields like books, news, music, videos, and movies, among others. These search engines allow users to find movies of their choice. K-mean, KNN, and hybrid algorithms have been covered in this study. K-means algorithm results based on metrics like "average Genre Rating" and "User Movie Rating". The RMSE feature has been used to KNN algorithm. A hybrid algorithm combines the two algorithms mentioned above. K-means is given an input, and the output of this method serves as the input for the KNN algorithm, which is more accurate than both K-means and KNN.

##plugins.themes.academic_pro.article.details##

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Author Biographies

Rupali Bagate, Army Institute of Technology

Rupali Bagate, is Research Scholar at department of Computer Science & Engineering, Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology, Chennai. The Author is working as Assistant Professor at the Department of Information Technology, Army Institute of Technology, Dighi Hills, Pune. Her area of research interest is Natural Language processing, Machine Learning, Deep Learning and Big data.

Aparna Joshi, Army Institute of Technology

Aparna Shashikant Joshi, is a Research Scholar at Department of Computer Science & Engineering, Vel Tech Rangarajan Dr. Sagunthala R\&D Institute of Science and Technology Chennai, India. Author working as an Assistant Professor at Department of Information Technology, Army Institute of Technology, Pune. Authors area of interests is cloud computing and Machine Learning.

Shilpa Pawar, Army Institute of Technology

Shilpa Devram Pawar, was born in Mumbai. She received her BE degree in Electronics and Telecommunication in June 1999 from Dr BAMU, Aurangabad, MS, India and ME degree in Electronics and Telecommunication in June 2011 from SPPU, Pune, MS, India. She is currently working as Assistant Professor in Electronics and Telecommunication Department in Army Institute of Technology, Pune, MS, India. She is currently pursuing the Ph.D in Electronics and Telecommunication from SPPU, Pune research center VIT, Pune, India. Research area is Embedded System and IOT.

Sharayu Lokhande, Army Institute of Technology

Ms Sharayu Ashishkumar Lokhande , Masters in Computer Engineering from Pune University.22 years of experience in academic sector .Perusing PhD . International Journal Publications : 15 ,International Conference publications : 5 ,National Conference Publications : 2 .Research area in Big Data, Data analytics , IoT, Machine Learning . Done almost 10 MOOC courses which have harvest my Technical Profile.I have been voluntarily contributed as a reviewer at many publications, some of the reputed publications like IEEE. Done more than 10 workshops to enhance the technical competency from Ministry of Electronics and Information Technology, Government of India and Pune University. Started YouTube channel in the Year 2017 on the topic “Academic Study of Database Management System” towards the contribution in the society.

How to Cite
Bagate, R., Joshi, A. ., Pawar, S. ., Hambir, Y. ., & Lokhande, S. (2022). Movie Recommendation Using Clustering and Nearest Neighbour. International Journal of Next-Generation Computing, 13(3). https://doi.org/10.47164/ijngc.v13i3.845
Crossref
Scopus
Google Scholar
Europe PMC

References

  1. Pazzani, Michael J. "A framework for collaborative, content-based and demographic filtering." Artificial intelligence review13.5-6 (1999): 393-408. DOI: https://doi.org/10.1023/A:1006544522159
  2. Linden, Greg, Brent Smith, and Jeremy York. "Amazon. com recommendations: Item-to-item collaborative filtering." IEEE Internet computing 1 (2003): 76-80. DOI: https://doi.org/10.1109/MIC.2003.1167344
  3. Kumar, Manoj, et al. "A movie recommender system: Movrec." International Journal of Computer Applications 124.3 (2015). DOI: https://doi.org/10.5120/ijca2015904111
  4. Sadanand, Howal, et al. "Movie Recommender Engine Using Collaborative Filtering." Smart Computing and Informatics. Springer, Singapore, 2018. 599-608. DOI: https://doi.org/10.1007/978-981-10-5547-8_62
  5. Sharma, Meenakshi, and Sandeep Mann. "A survey of recommender systems: approaches and limitations." International Journal of Innovations in Engineering and Technology 2.2 (2013): 8-14.
  6. Cui, Bei-Bei. "Design and Implementation of Movie Recommendation System Based on Knn Collaborative Filtering Algorithm." ITM Web of Conferences. Vol. 12. EDP Sciences, 2017. DOI: https://doi.org/10.1051/itmconf/20171204008
  7. Van Meteren, Robin, and Maarten Van Someren. "Using content-based filtering for recommendation." Proceedings of the Machine Learning in the New Information Age: MLnet/ECML2000 Workshop. 2000.
  8. Ekstrand, Michael D., John T. Riedl, and Joseph A. Konstan. "Collaborative filtering recommender systems." Foundations and Trends® in Human–Computer Interaction 4.2 (2011): 81-173. DOI: https://doi.org/10.1561/1100000009
  9. Shridhar, S., Lakhanpuria, M., Charak, A., Gupta, A. and Shridhar, S., 2012, November. SNAIR: a framework for personalised recommendations based on social network analysis. In Proceedings of the 5th ACM SIGSPATIAL International Workshop on Location-Based Social Networks (pp. 55-61).
  10. Gandhi, Sonali R., and Jaydeep Gheewala. "A survey on recommendation system with collaborative filtering using big data." 2017 International Conference on Innovative Mechanisms for Industry Applications (ICIMIA). IEEE, 2017. DOI: https://doi.org/10.1109/ICIMIA.2017.7975657
  11. Geetha, G., et al. "A hybrid approach using collaborative filtering and content based filtering for recommender system." Journal of Physics: Conference Series. Vol. 1000. No. 1. IOP Publishing, 2018. DOI: https://doi.org/10.1088/1742-6596/1000/1/012101
  12. Gunawardana, Asela, and Guy Shani. "A survey of accuracy evaluation metrics of recommendation tasks." Journal of Machine Learning Research 10.Dec (2009): 2935-2962.
  13. Bhatt, Bhumika, J. Patel Premal, and Hetal Gaudani. "A Review Paper on Machine Learning Based Recommendation System 1." (2014).
  14. Ji, Hao, et al. "Hybrid collaborative filtering model for improved recommendation." Proceedings of 2013 IEEE International Conference on Service Operations and Logistics, and Informatics. IEEE, 2013. DOI: https://doi.org/10.1109/SOLI.2013.6611398
  15. Himel, Md Tayeb, et al. "Weight based movie recommendation system using K-means algorithm." 2017 International Conference on Information and Communication Technology Convergence (ICTC). IEEE, 2017. DOI: https://doi.org/10.1109/ICTC.2017.8190928
  16. Wang, Zan, et al. "An improved collaborative movie recommendation system using computational intelligence." Journal of Visual Languages & Computing 25.6 (2014): 667-675. DOI: https://doi.org/10.1016/j.jvlc.2014.09.011
  17. Han, Jiawei, Jian Pei, and Micheline Kamber. Data mining: concepts and techniques. Elsevier, 2011.
  18. ”Movielens dataset,”http://www.grouplens.org/data/