Methods for recommendation

In the previous section, we described the use case of building a movie recommendation engine for the company ZHO and also prepared SPSS on the Spark computing platform. In this section, as before, we need to select our analytical methods (equations) for this movie recommendation project, which again means mapping our use case to machine learning methods.

For this exercise, we will use collaborative filtering because this analytical method is well developed and tested on many recommendation projects. At the same time, analytical processes and related algorithms are also well-developed for this method, which are available in R as well as MLlib.

By following the same methodology, once we finalize our decision for analytical methods or models, we will then need to prepare the coding.

Collaborative filtering

Collaborative filtering is a method used very commonly to build recommender systems. Simply speaking, collaborative filtering is an analytical method of producing predictions...