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[SPARK-2843][MLLIB] add a section about regularization parameter in ALS #2064

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[SPARK-2843][MLLIB] add a section about regularization parameter in ALS #2064

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add a section about regularization parameter in ALS
mengxr Aug 20, 2014
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add reference
mengxr Aug 20, 2014
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introduced -> discussed
mengxr Aug 20, 2014
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11 changes: 11 additions & 0 deletions docs/mllib-collaborative-filtering.md
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Expand Up @@ -43,6 +43,17 @@ level of confidence in observed user preferences, rather than explicit ratings g
model then tries to find latent factors that can be used to predict the expected preference of a
user for an item.

### Scaling of the regularization parameter

Since v1.1, we scale the regularization parameter `lambda` in solving each least squares problem by
the number of ratings the user generated in updating user factors,
or the number of ratings the product received in updating product factors.
This approach is named "ALS-WR" and introduced in the paper
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This technique was used by many groups in the Netflix Prize (see section 5 of this paper for some more references: http://www.cs.toronto.edu/~rsalakhu/papers/weighted_tc.pdf). Perhaps this sentence should be changed to "This approach is discussed in the following paper..."

"[Large-Scale Parallel Collaborative Filtering for the Netflix Prize](http://dx.doi.org/10.1007/978-3-540-68880-8_32)".
It makes `lambda` 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.

## Examples

<div class="codetabs">
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