Abstract
Recommendation systems for suggesting products are crucial, particularly in streaming services. Recommendation algorithms are crucial for helping viewers find new movies they like on streaming movie platforms like Netflix. In this chapter, we create a smart algorithm that makes an optimistic choice to design a collaborative filtering system that forecasts movie ratings for a user based on a significant database of user ratings. According to the genres that users like to watch, it suggests movies that are the greatest fit for them. The cumulative influence of user ratings and reviews produces the list of suggested films. A statistical analysis is performed to develop a pilot survey model to analyze the real-time dataset. Ant Colony Optimization (ACO) is deployed to determine the rating of the group members’ for future recommendation. In this way, sparsity problems will be optimized in a recommender system. A real-time dataset named as Movielens is used to validate the proposed model. Finally, deploy k-fold cross validation to evaluate the performance metric.
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Sarkar, M., Singh, S., Soundarya, V.L., Agrebi, M., Alkhayyat, A. (2023). An Intelligent Model for Optimizing Sparsity Problem Toward Movie Recommendation Paradigm Using Machine Learning. In: Sharma, R., Jeon, G., Zhang, Y. (eds) Data Analytics for Internet of Things Infrastructure. Internet of Things. Springer, Cham. https://doi.org/10.1007/978-3-031-33808-3_10
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