Abstract
Recently, the application of machine learning algorithms is very useful in marketing by companies nowadays. Overall, it has become a big factor on the companies success and growth in term of the number of users or revenues, since it helps to suggest the right content to the right people in an easy way without going through a long complicated process to choose an element in a list of millions elements. This research has a goal of evaluating several recommending mining algorithms in machine learning by adopting a model that combines the content-based (constrained system to people) and collaborative approach and compares it with a paralleled algorithm, and we assume that can help to get the right recommendations to users. The model’s results show that it can positively solve this issue and help users to find the right content that they want to watch, and also predict if they like the new trending content.
Keywords
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Adomavicius, G., Tuzhilin, A.: Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. IEEE Trans. Knowl. Data Eng. 17, 734–749 (2005). https://doi.org/10.1109/TKDE.2005.99
Chen, L., Hsu, F., Chen, M., Hsu, Y.: Developing recommender systems with the consideration of product profitability for sellers. Inf. Sci. 178, 1032–1048 (2008). https://doi.org/10.1016/j.ins.2007.09.027
Steck, H.: Evaluation of recommendations: rating-prediction and ranking. In: Proceedings of the 7th ACM conference on Recommender systems. pp. 213-220. Association for Computing Machinery, New York (2013). https://doi.org/10.1145/2507157.2507160
Zheng, Y., Agnani, M., Singh, M.: Identification of grey sheep users by histogram intersection in recommender systems. In: Cong, G., Peng, W.-C., Zhang, W.E., Li, C., Sun, A. (eds.) ADMA 2017. LNCS (LNAI), vol. 10604, pp. 148–161. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-69179-4_11
Grčar, M., Mladenič, D., Fortuna, B., Grobelnik, M.: Data sparsity issues in the collaborative filtering framework. In: Nasraoui, O., Zaïane, O., Spiliopoulou, M., Mobasher, B., Masand, B., Yu, P.S. (eds.) WebKDD 2005. LNCS (LNAI), vol. 4198, pp. 58–76. Springer, Heidelberg (2006). https://doi.org/10.1007/11891321_4
Rahul, M., Kumar, V., Yadav, V., Rishabh: Movie recommender system using single value decomposition and K-means Clustering. In: IOP Conference Series: Materials Science and Engineering, vol. 1022, p. 012100 (2021). https://doi.org/10.1088/1757-899X/1022/1/012100
Kumar, M.S., Prabhu, J.: A hybrid model collaborative movie recommendation system using K-means clustering with ant colony optimisation. Int. J. Internet Technol. Secured Trans. 10, 337–354 (2020). https://doi.org/10.1504/IJITST.2020.107079
Ahuja, R., Solanki, A., Nayyar, A.: Movie recommender system using K-means clustering AND K-nearest neighbor. In: 2019 9th International Conference on Cloud Computing, Data Science Engineering (Confluence), pp. 263–268 (2019). https://doi.org/10.1109/CONFLUENCE.2019.8776969
Croft, W.B., Metzler, D., Strohman, T.: Search Engines: Information Retrieval in Practice. Addison-Wesley, Boston (2010)
Chen, H.-W., Wu, Y.-L., Hor, M.-K., Tang, C.-Y.: Fully content-based movie recommender system with feature extraction using neural network. In: 2017 International Conference on Machine Learning and Cybernetics (ICMLC), pp. 504–509 (2017). https://doi.org/10.1109/ICMLC.2017.8108968
MovieLens 100K Dataset. https://grouplens.org/datasets/movielens/100k/. Accessed 20 Jan 2021
MovieLens 20M Dataset. https://grouplens.org/datasets/movielens/20m/. Accessed 20 Jan 2021
Zhouxiao, B., Haiying, X.: Movie Rating Estimation and Recommendation (2012)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Nesmaoui, R., Louhichi, M., Lazaar, M. (2022). A Hybrid Machine Learning Method for Movies Recommendation. In: Lazaar, M., Duvallet, C., Touhafi, A., Al Achhab, M. (eds) Proceedings of the 5th International Conference on Big Data and Internet of Things. BDIoT 2021. Lecture Notes in Networks and Systems, vol 489. Springer, Cham. https://doi.org/10.1007/978-3-031-07969-6_39
Download citation
DOI: https://doi.org/10.1007/978-3-031-07969-6_39
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-07968-9
Online ISBN: 978-3-031-07969-6
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)