Abstract
Recommendation systems are advanced data analytical applications where the users are provided with various recommendations based on their preferences. They are used in various online retail sites for identifying the purchasing behavior of the users and ratings of different products. The different types of recommendation systems are content-based recommendation system, knowledge-based recommendation system, and collaborative filtering. Collaborative filtering is one of the widely used methods in recommendation systems. There are many techniques for collaborative filtering of recommendation systems. Matrix factorization is one of such methods where recommendations are based on the ratings of the products by the users. In this chapter, an overview of recommendation system followed by the matrix factorization method is discussed. A case study on the movielens dataset is discussed as an example for recommendation system with Apache Spark.
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Srinivasa, K.G., G. M., S., H., S. (2018). Recommendation Systems. In: Network Data Analytics. Computer Communications and Networks. Springer, Cham. https://doi.org/10.1007/978-3-319-77800-6_15
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DOI: https://doi.org/10.1007/978-3-319-77800-6_15
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