[GitHub] spark pull request #17102: [SPARK-19345][ML][DOC] Add doc for "coldStartStra...

[sethah <gi...@git.apache.org>]

Github user sethah commented on a diff in the pull request:

    https://github.com/apache/spark/pull/17102#discussion_r103731183
  
    --- Diff: docs/ml-collaborative-filtering.md ---
    @@ -59,6 +59,34 @@ This approach is named "ALS-WR" and discussed in the paper
     It makes `regParam` less dependent on the scale of the dataset, so we can apply the
     best parameter learned from a sampled subset to the full dataset and expect similar performance.
     
    +### Cold-start strategy
    +
    +When making predictions using an `ALSModel`, it is common to encounter users and/or items in the 
    +test dataset that were not present during training the model. This typically occurs in two 
    +scenarios:
    +
    +1. In production, for new users or items that have no rating history and on which the model has not 
    +been trained (this is the "cold start problem")
    +2. During cross-validation, the data is split between training and evaluation sets. When using 
    +simple random splits as in Spark's `CrossValidator` or `TrainValidationSplit`, it is actually 
    +very common to encounter users and/or items in the evaluation set that are not in the training set
    +
    +By default, Spark assigns `NaN` predictions during `ALSModel.transform` when a user and/or item 
    +factor is not present in the model. This can be useful in a production system, since it indicates 
    +a new user or item, and so the system can make a decision on some fallback to use as the prediction.
    +
    +However, this is undesirable during cross-validation, since any `NaN` predicted values will result
    +in `NaN` results for the evaluation metric (for example when using `RegressionEvaluator`).
    +This makes model selection impossible.
    +
    +Spark allows users to set the `coldStartStrategy` parameter
    +to `drop` in order to drop any rows in the `DataFrame` of predictions that contain `NaN` values. 
    +The evaluation metric will then be computed over the non-`NaN` data and will be valid. 
    +Usage of this parameter is illustrated in the example below.
    +
    +**Note:** currently the supported cold start strategies are `nan` (the default behavior mentioned 
    --- End diff --
    
    A bit wary of putting the options explicitly here, but it seems hard to avoid since they're mentioned above. Even so, maybe use "drop" and "nan" (quotes).


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