Project: Apache Spark (App Definition and Development / Streaming & Messaging)
File: ml-collaborative-filtering.md

layout: global title: Collaborative Filtering displayTitle: Collaborative Filtering license: | Licensed to the Apache Software Foundation (ASF) under one or more contributor license agreements. See the NOTICE file distributed with this work for additional information regarding copyright ownership. The ASF licenses this file to You under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. Table of contents {:toc} 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)](http://dl.acm.org/citation.cfm?id=1608614) 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 (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`). 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. 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

views, clicks, purchases, likes, shares etc.). The approach used in `spark.ml` to deal with such data is taken from . 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 "". 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. When making predictions using an `ALSModel`, it is common to encounter users and/or items in the test dataset that were not present during training the model. This typically occurs in two scenarios: In production, for new users or items that have no rating history and on which the model has not been trained (this is the "cold start problem"). During cross-validation, the data is split between training and evaluation sets. When using simple random splits as in Spark's `CrossValidator` or `TrainValidationSplit`, it is actually very common to encounter users and/or items in the evaluation set that are not in the training set By default, Spark assigns `NaN` predictions during `ALSModel.transform` when a user and/or item factor is not present in the model. This can be useful in a production system, since it indicates a new user or item, and so the system can make a decision on some fallback to use as the prediction. However, this is undesirable during cross-validation, since any `NaN` predicted values will result in `NaN` results for the evaluation metric (for example when using `RegressionEvaluator`). This makes model selection impossible. Spark allows users to set the `coldStartStrategy` parameter to "drop" in order to drop any rows in the `DataFrame` of predictions that contain `NaN` values. The evaluation metric will then be computed over the non-`NaN` data and will be

Usage of this parameter is illustrated in the example below. Note: currently the supported cold start strategies are "nan" (the default behavior mentioned above) and "drop". Further strategies may be supported in future. Examples <div class="codetabs"> <div data-lang="python" markdown="1"> In the following example, we load ratings data from the , 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 for more details on the API. {% includeexample python/ml/alsexample.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 %} </div> <div data-lang="scala" markdown="1"> In the following example, we load ratings data from the , 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 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 %} </div> <div data-lang="java" markdown="1"> In the following example, we load ratings data from the , 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 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 %} </div> <div data-lang="r" markdown="1"> Refer to the for more details. {% include_example r/ml/als.R %} </div> </div>