Generates an user-ranking-product associated matrix, based on given user-product interactive data.
This matrix is used by tRecommend to estimate these users' preferences.
tALSModel leverages Spark to process a large amount of information about users' preferences over given products.
It receives this kind of information from its preceding Spark component and performs ALS (Alternating Least Squares) computations over these sets of information in order to generate and write a fine-tuned product recommender model in a given file system in the Parquet format.
For more technologies supported by Talend, see Talend components.