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Diffstat (limited to 'docs/ml-collaborative-filtering.md')
| -rw-r--r-- | docs/ml-collaborative-filtering.md | 6 |
1 files changed, 5 insertions, 1 deletions
diff --git a/docs/ml-collaborative-filtering.md b/docs/ml-collaborative-filtering.md index bd3d527d9a..8bd75f3bcf 100644 --- a/docs/ml-collaborative-filtering.md +++ b/docs/ml-collaborative-filtering.md @@ -29,6 +29,10 @@ following parameters: *baseline* confidence in preference observations (defaults to 1.0). * *nonnegative* specifies whether or not to use nonnegative constraints for least squares (defaults to `false`). +**Note:** The DataFrame-based API for ALS currently only supports integers for user and item ids. +Other numeric types are supported for the user and item id columns, +but the ids must be within the integer value range. + ### Explicit vs. implicit feedback The standard approach to matrix factorization based collaborative filtering treats @@ -36,7 +40,7 @@ the entries in the user-item matrix as *explicit* preferences given by the user for example, users giving ratings to movies. It is common in many real-world use cases to only have access to *implicit feedback* (e.g. views, -clicks, purchases, likes, shares etc.). The approach used in `spark.mllib` to deal with such data is taken +clicks, purchases, likes, shares etc.). The approach used in `spark.ml` to deal with such data is taken from [Collaborative Filtering for Implicit Feedback Datasets](http://dx.doi.org/10.1109/ICDM.2008.22). Essentially, instead of trying to model the matrix of ratings directly, this approach treats the data as numbers representing the *strength* in observations of user actions (such as the number of clicks, |