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:
false which means using explicit feedback).false).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.
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.
In the following example, 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.
Refer to the ALS Scala docs for more details on the API.
{% include_example scala/org/apache/spark/examples/ml/ALSExample.scala %}
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:
{% highlight scala %} val als = new ALS() .setMaxIter(5) .setRegParam(0.01) .setImplicitPrefs(true) .setUserCol(“userId”) .setItemCol(“movieId”) .setRatingCol(“rating”) {% endhighlight %}
In the following example, 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.
Refer to the ALS Java docs for more details on the API.
{% include_example java/org/apache/spark/examples/ml/JavaALSExample.java %}
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:
{% highlight java %} ALS als = new ALS() .setMaxIter(5) .setRegParam(0.01) .setImplicitPrefs(true) .setUserCol(“userId”) .setItemCol(“movieId”) .setRatingCol(“rating”); {% endhighlight %}
In the following example, 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.
Refer to the ALS Python docs for more details on the API.
{% include_example python/ml/als_example.py %}
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:
{% highlight python %} als = ALS(maxIter=5, regParam=0.01, implicitPrefs=True, userCol=“userId”, itemCol=“movieId”, ratingCol=“rating”) {% endhighlight %}