Awan, Mazhar Javed, Khan, Rafia Asad, Nobanee, Haitham 
ORCID: 0000-0003-4424-5600, Yasin, Awais, Anwar, Syed Muhammad, Naseem, Usman and Singh, Vishwa Pratap
(2021)
A Recommendation Engine for Predicting Movie Ratings Using a Big Data Approach.
ELECTRONICS, 10 (10).
p. 1215.
Abstract
<jats:p>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.</jats:p>
| Item Type: | Article |
|---|---|
| Additional Information: | Source info: Electronics 2021,10, 1215 |
| Uncontrolled 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 |
| Divisions: | Faculty of Humanities and Social Sciences > School of Histories, Languages and Cultures |
| Depositing User: | Symplectic Admin |
| Date Deposited: | 21 Jan 2022 16:34 |
| Last Modified: | 15 Mar 2024 18:12 |
| DOI: | 10.3390/electronics10101215 |
| Open Access URL: | https://www.mdpi.com/2079-9292/10/10/1215 |
| Related URLs: | |
| URI: | https://livrepository.liverpool.ac.uk/id/eprint/3147359 |
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