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Article

A Recommendation Engine for Predicting Movie Ratings Using a Big Data Approach

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Department of Software Engineering, University of Management and Technology, Lahore 54770, Pakistan
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Department of Computer Science, University of Management and Technology, Lahore 54770, Pakistan
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College of Business, Abu Dhabi University, Abu Dhabi 59911, United Arab Emirates
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Oxford Centre for Islamic Studies, University of Oxford, Marston Rd, Headington, Oxford OX3 0EE, UK
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School of Histories, Languages, and Cultures, University of Liverpool, 12 Abercromby Square, Liverpool L69 7WZ, UK
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Department of Computer Engineering, National University of Technology, Islamabad 44000, Pakistan
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Department of Software Engineering, University of Engineering and Technology, Taxila 47050, Pakistan
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School of Computer Science, The University of Sydney, Sydney, NSW 2006, Australia
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University School of Information, Communication and Technology, Guru Gobind Indraprastha University, Delhi 110078, India
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Authors to whom correspondence should be addressed.
Academic Editor: Rashid Mehmood
Electronics 2021, 10(10), 1215; https://doi.org/10.3390/electronics10101215
Received: 5 May 2021 / Revised: 14 May 2021 / Accepted: 17 May 2021 / Published: 20 May 2021
(This article belongs to the Special Issue Big Data Privacy-Preservation)
In this era of big data, the amount of video content has dramatically increased with an exponential broadening of video streaming services. Hence, it has become very strenuous for end-users to search for their desired videos. Therefore, to attain an accurate and robust clustering of information, a hybrid algorithm was used to introduce a recommender engine with collaborative filtering using Apache Spark and machine learning (ML) libraries. In this study, we implemented a movie recommendation system based on a collaborative filtering approach using the alternating least squared (ALS) model to predict the best-rated movies. Our proposed system uses the last search data of a user regarding movie category and references this to instruct the recommender engine, thereby making a list of predictions for top ratings. The proposed study used a model-based approach of matrix factorization, the ALS algorithm along with a collaborative filtering technique, which solved the cold start, sparse, and scalability problems. In particular, we performed experimental analysis and successfully obtained minimum root mean squared errors (oRMSEs) of 0.8959 to 0.97613, approximately. Moreover, our proposed movie recommendation system showed an accuracy of 97% and predicted the top 1000 ratings for movies. View Full-Text
Keywords: recommendation engine; Spark machine learning; filtering; collaborative filtering; RMSE; Pyspark; matrix factorization; oRMSE; ALS (alternating least squared); Apache Spark; Spark ML Movielens dataset; Spark MLlib recommendation engine; Spark machine learning; filtering; collaborative filtering; RMSE; Pyspark; matrix factorization; oRMSE; ALS (alternating least squared); Apache Spark; Spark ML Movielens dataset; Spark MLlib
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MDPI and ACS Style

Awan, M.J.; Khan, R.A.; Nobanee, H.; Yasin, A.; Anwar, S.M.; Naseem, U.; Singh, V.P. A Recommendation Engine for Predicting Movie Ratings Using a Big Data Approach. Electronics 2021, 10, 1215. https://doi.org/10.3390/electronics10101215

AMA Style

Awan MJ, Khan RA, Nobanee H, Yasin A, Anwar SM, Naseem U, Singh VP. A Recommendation Engine for Predicting Movie Ratings Using a Big Data Approach. Electronics. 2021; 10(10):1215. https://doi.org/10.3390/electronics10101215

Chicago/Turabian Style

Awan, Mazhar J., Rafia A. Khan, Haitham Nobanee, Awais Yasin, Syed M. Anwar, Usman Naseem, and Vishwa P. Singh 2021. "A Recommendation Engine for Predicting Movie Ratings Using a Big Data Approach" Electronics 10, no. 10: 1215. https://doi.org/10.3390/electronics10101215

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