Alternating least squares and collaborative filtering in spark.ml
February 15, 2016
-
Machine Learning,
Tutorial,
Spark
In this post, I’ll show you how to use alternating least squares (ALS for short)
in spark.ml.
Disclaimer: This post is mostly a copy/paste from
a pull request I wrote for Spark
documenting ALS and collaborative filtering in general in spark.ml.
Since the PR will likely be incorporated in the 2.0 release which is still a few
months away, I thought I’d share it. This is also in response to this
stackoverflow question asking about documentation
regarding collaborative filtering in spark.ml.
Collaborative filtering
Collaborative filtering
is commonly used for recommender systems. These techniques aim to fill in the
missing entries of a user-item association matrix. spark.ml currently supports
model-based collaborative filtering, in which users and products are described
by a small set of latent factors that can be used to predict missing entries.
spark.ml uses the alternating least squares
(ALS)
algorithm to learn these latent factors.
The implementation in spark.ml has the following parameters:
numBlocks is the number of blocks the users and items will be partitioned
into in order to parallelize computation (defaults to 10).
rank is the number of latent factors in the model (defaults to 10).
maxIter is the maximum number of iterations to run (defaults to 10).
regParam specifies the regularization parameter in ALS (defaults to 1.0).
implicitPrefs specifies whether to use the explicit feedback ALS variant
or one adapted for implicit feedback data, see more below (defaults to false
which means using explicit feedback).
alpha is a parameter applicable to the implicit feedback variant of ALS that
governs the baseline confidence in preference observations (defaults to 1.0).
nonnegative specifies whether or not to use nonnegative constraints for
least squares (defaults to false).
Explicit vs. implicit feedback
The standard approach to matrix factorization based collaborative filtering
treats the entries in the user-item matrix as explicit preferences given by
the user to the item, 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.ml to deal with such data is taken from
Collaborative Filtering for Implicit Feedback Datasets.
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, or the cumulative duration
someone spent viewing a movie). Those numbers are then related to the level of
confidence in observed user preferences, rather than explicit ratings given to
items. The 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
We scale the regularization parameter regParam 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 discussed in the paper
“Large-Scale Parallel Collaborative Filtering for the Netflix Prize”.
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.
Examples
In the following examples, we load rating data from the
MovieLens dataset, each row
consisting of a user, a movie, a rating and a timestamp.
We then train an ALS model which assumes, by default, that the ratings are
explicit (implicitPrefs is false).
We evaluate the recommendation model by measuring the root-mean-square error of
rating prediction.
Scala example
You can have a look at the
ALS Scala docs
for more details on the API.
If the rating matrix is derived from another source of information (i.e. it is
inferred from other signals), you can set implicitPrefs to true to get
better results:
Java example
You can have a look at the
ALS Java docs
for more details on the API.
In Java as well, if the rating matrix is derived from another source of
information (i.e. it is inferred from other signals), you can set
implicitPrefs to true to get better results:
Python example
You can have a look at the
ALS Python docs
for more details on the API.
Same in Python, if the rating matrix is derived from another source of
information (i.e. it is inferred from other signals), you can set
implicitPrefs to True to get better results:
Conclusion
You can find the full examples and the scripts to run them on my repo
sparkml-als.
Hoping this was informative and made you want to try out ALS in spark.ml.