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.
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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
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