Abstract
Recommendation engines are very useful for businesses to increase their revenue. They are regarded as one of the best types of machine learning models. They are responsible for predicting the choices of people and uncovering all the relationships between items so that discovery of the right choices becomes easy. They help in presenting users with items that they might not even have searched or have known about. Movies are considered as one of the most popular sources of entertainment. It is a very tedious task to search for movies according to the user’s taste from a large pool of available movies. The proposed system builds a movie recommendation engine that uses the user’s profile to find movies of similar taste as the user. The system recommends the most relevant movies to the user. Apache Spark framework is used for implementing the proposed system via Scala language. The Apache Spark machine learning library (MLlib) is used to ease the implementation. The proposed system provides analysis on various measures. The measures include the total number of ratings by a user, top ten recommended movie ids and names with predicted ratings for a particular user. At last, the performance of the system is evaluated using Root Mean Square Error (RMSE). The value of RMSE gives the accuracy of the model. The results are shown in tabular form. The results show that the model well and after some number of iterations, the value of RMSE is constant.
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Malik, A., Gupta, H., Kumar, G., Sharma, R.K. (2023). Rating of Movie via Movie Recommendation System Based on Apache Spark Using Big Data and Machine Learning Techniques. In: Chatterjee, P., Pamucar, D., Yazdani, M., Panchal, D. (eds) Computational Intelligence for Engineering and Management Applications. Lecture Notes in Electrical Engineering, vol 984. Springer, Singapore. https://doi.org/10.1007/978-981-19-8493-8_55
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DOI: https://doi.org/10.1007/978-981-19-8493-8_55
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