Collaborative filtering is commonly used for recommender systems. These techniques aim to fill in the missing entries of a user-item association matrix. spark.mllib
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.mllib
uses the alternating least squares (ALS) algorithm to learn these latent factors. The implementation in spark.mllib
has the following parameters:
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.mllib
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.
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 discussed in the paper “Large-Scale Parallel Collaborative Filtering for the Netflix Prize”. 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.
Refer to the ALS
Scala docs for more details on the API.
{% include_example scala/org/apache/spark/examples/mllib/RecommendationExample.scala %}
If the rating matrix is derived from another source of information (i.e. it is inferred from other signals), you can use the trainImplicit
method to get better results.
{% highlight scala %} val alpha = 0.01 val lambda = 0.01 val model = ALS.trainImplicit(ratings, rank, numIterations, lambda, alpha) {% endhighlight %}
Refer to the ALS
Java docs for more details on the API.
{% include_example java/org/apache/spark/examples/mllib/JavaRecommendationExample.java %}
Refer to the ALS
Python docs for more details on the API.
{% include_example python/mllib/recommendation_example.py %}
If the rating matrix is derived from other source of information (i.e. it is inferred from other signals), you can use the trainImplicit method to get better results.
{% highlight python %}
model = ALS.trainImplicit(ratings, rank, numIterations, alpha=0.01) {% endhighlight %}
In order to run the above application, follow the instructions provided in the Self-Contained Applications section of the Spark Quick Start guide. Be sure to also include spark-mllib to your build file as a dependency.
The training exercises from the Spark Summit 2014 include a hands-on tutorial for personalized movie recommendation with spark.mllib
.