Abstract
The rising demand for high-quality movie recommendations in streaming services necessitates more efficient algorithms capable of handling large datasets. Traditional recommendation systems often struggle with long training times and high computational costs. This study introduces a novel movie recommendation system utilizing a quantum support vector machine (QSVM) to overcome these limitations. By leveraging quantum algorithms, QSVM enhances both the speed and accuracy of recommendations. Our approach involves collecting and preprocessing data, implementing classical SVM for baseline comparison, encoding data for QSVM, and executing QSVM using a publicly accessible IBM quantum computer. The results demonstrate that QSVM outperforms classical SVM, achieving a 96% accuracy and an F1 score of 0.9693, compared to the classical SVM’s 95.33% accuracy and 0.9641 F1 score. This signifies QSVM’s superior capability in handling complex datasets. Our findings highlight the potential of QSVM in movie recommendation systems, suggesting future research directions in quantum machine learning and its applications.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.Availability of data and materials
The datasets used and/or analyzed during the current study are publicly available at the following links: IMDB Movies Dataset https://www.kaggle.com/datasets/ashishjangra27/imdb-movies-dataset IMDB Spoiler Dataset https://www.kaggle.com/datasets/rmisra/imdb-spoiler-dataset.
Code availability
Not applicable.
References
Abdullah ASHFSWLHY, Abdullah ANK (2021) Evolution in computational intelligence: frontiers in intelligent computing: theory and applications (FICTA 2020), 1, Springer Singapore
Al Amrani YLM, Al Amrani EKKE (2018) Random forest and support vector machine based hybrid approach to sentiment analysis. Proced Comput Sci 127:511–520
Behera DK, Das M, Swetanisha S, Sethy PK (2021) Hybrid model for movie recommendation system using content K-nearest neighbors and restricted Boltzmann machine Indonesian Journal of Electrical Engineering and Computer. Science 23(1):445–452
Chopra S, Veeraiyan M (2017). Movie rental business: blockbuster, Netflix, and redbox. Kellogg School of Management Cases, pp 1–21
Ding C, Bao T, Huang H (2021) Quantum-inspired support vector machine. IEEE Trans Neural Networks Learning Syst 33(12):7210–7222
Drif A, Guembour S, Cherifi H (2021). Complex Networks & Their Applications IX: Volume 2. In: Proceedings of the NInth International Conference on Complex Networks and Their Applications Complex Networks 2020. Springer
Fang JaWX (2020). Impact of digital technology and internet to film industry. In: IOP Conference Series: Materials Science and Engineering
Frykowska A, Zbieć I, Kacperski P, Vesely P, Studenicova A (2019). Movies recommendation system movies recommendation system. In: International Conference on Intelligent Networking and Collaborative Systems
Gao Y, Shen S, Gao S (2010). An empirical evaluation of linear and nonlinear kernels for text classification using support vector machines. In: Proceedings of the 7th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD), p 4
Gilbert A (2019) Push, pull, rerun: television reruns and streaming media. Telev New Media 20(7):686–701
Havlicek V, Córcoles ÁD, Temme K, Harrow AW, Kandala A, Moll N, Troyer M (2019) Supervised learning with quantum-enhanced feature spaces. Nature 567(7747):209–212
Jangra A (2021). IMDB Movies Dataset.[SPACE]https://www.kaggle.com/datasets/ashishjangra27/imdb-movies-dataset Accessed: 2024-09-23
Jayanth SB, Anthony A, Abhilasha G, Shaik N, Srinivasa G (2018) A team recommendation system and outcome prediction for the game of cricket. J Sp Anal 4(4):263–273
Kariya A, Behera BK (2021). Investigation of quantum support vector machine for classification in NISQ era
Kavitha SS, Kaulgud N (2022) Quantum machine learning for support vector machine classification. Evolut Intell 17(2):819–828
Misra R (2020). IMDB Spoiler Dataset.[SPACE]https://www.kaggle.com/datasets/rmisra/imdb-spoiler-dataset Accessed: 2024-09-23
Park J-E, Quanz B, Wood S, Higgins H, Harishankar R (2020). Practical application improvement to Quantum SVM: theory to practice. arXiv preprint arXiv:2012.07725
Reddy MM, Kanmani RS, Surendiran B (2020). Analysis of Movie Recommendation Systems; with and without considering the low rated movies. In: 2020 International Conference on Emerging Trends in Information Technology and Engineering (icETITE)
Zea Ren (2021) A sentiment-aware deep learning approach for personality detection from text. Inf Process Manag 58(3):102532
Rospitasari M (2021) Youtube as alternative media for digital activism in documentary film creative industry. J Studi Komunikasi 5(3):665–692
Schuld M (2021). Supervised quantum machine learning models are kernel methods. arXiv:2106.03150
Schuld M, Fingerhuth M, Petruccione F (2019) The quest for a quantum neural network. Quantum Inf Process 18(7):1–39
Schuld M, Killoran N (2019) Quantum machine learning in feature hilbert spaces. Phys Rev Lett 122(4):040504
Shahbazi Z, Hazra D, Park S, Byun YC (2020) Toward improving the prediction accuracy of product recommendation system using extreme gradient boosting and encoding approaches. Symmetry 12(9):1566
Sharma N, Dutta M (2020). Movie recommendation systems: A brief overview. In: Proceedings of the 8th International Conference on Computer and Communications Management
Sheik Abdullah Aea (2021). Evolution in computational intelligence: frontiers in intelligent computing: theory and applications (FICTA 2020), 1. Springer Singapore
Shelke PPaANK (2020). 2020 Fourth International Conference on Computing Methodologies and Communication (ICCMC). IEEE. Ieee
Silveira T, Zhang M, Lin X, Liu Y, Ma S (2019) How good your recommender system is? A survey on evaluations in recommendation. Int J Mach Learn Cybern 10:813–831
Tang E (2019). A quantum-inspired classical algorithm for recommendation systems. In: Proceedings of the 51st Annual ACM SIGACT Symposium on Theory of Computing
Wang Y, Wang M, Xu W (2018) (2018) A sentiment-enhanced hybrid recommender system for movie recommendation: a big data analytics framework. Wirel Commun Mobile Comput 1:8263704
Funding
This research is funded by the European University of Atlantic.
Author information
Authors and Affiliations
Contributions
MS conceived the idea, performed formal analysis and wrote the original draft. MAH performed data curation, formal analysis, and wrote the original draft. FA designed methodology, and performed investigation and formal analysis. AA dealt with software, performed visualization and carried out project administration. SWHS conceived the idea, performed data curation and performed visualization. AVE acquired the funding and performed visualization, and investigation. IA supervised the study, performed validation and review and edit the manuscript. All authors read and approved the final manuscript.
Corresponding authors
Ethics declarations
Conflict of interest
The authors declare no Conflict of interest.
Ethical approval
Not applicable
Consent to participate
Not applicable
Consent for publication
Not applicable
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Shahid, M., Hassan, M.A., Iqbal, F. et al. Enhancing movie recommendations using quantum support vector machine (QSVM). J Supercomput 81, 78 (2025). https://doi.org/10.1007/s11227-024-06501-2
Accepted:
Published:
DOI: https://doi.org/10.1007/s11227-024-06501-2