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Machine Learning with Spark - Second Edition by Nick Pentreath, Manpreet Singh Ghotra, Rajdeep Dua

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Training a model using Implicit feedback data

The standard Matrix Factorization approach in MLlib deals with explicit ratings. To work with implicit data, you can use the trainImplicit method. It is called in a manner similar to the standard train method. There is an additional parameter, alpha, that can be set (and in the same way, the regularization parameter, lambda, should be selected via testing and cross-validation methods).

The alpha parameter controls the baseline level of confidence, weighting applied. A higher level of alpha tends to make the model more confident about the fact that missing data equates to no preference for the relevant user-item pair.

From Spark version 2.0, if the rating matrix is derived from another source of ...

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