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diff --git a/docs/mllib-collaborative-filtering.md b/docs/mllib-collaborative-filtering.md index 1ebb4654ae..b8f0566d87 100644 --- a/docs/mllib-collaborative-filtering.md +++ b/docs/mllib-collaborative-filtering.md @@ -31,17 +31,18 @@ following parameters: ### 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. +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](http://dx.doi.org/10.1109/ICDM.2008.22). -Essentially instead of trying to model the matrix of ratings directly, this approach treats the data -as a combination of binary preferences and *confidence values*. The ratings 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. +from [Collaborative Filtering for Implicit Feedback Datasets](http://dx.doi.org/10.1109/ICDM.2008.22). +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 @@ -50,9 +51,8 @@ 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](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. +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 @@ -64,11 +64,11 @@ We use the default [ALS.train()](api/scala/index.html#org.apache.spark.mllib.rec method which assumes ratings are explicit. We evaluate the recommendation model by measuring the Mean Squared Error of rating prediction. -Refer to the [`ALS` Scala docs](api/scala/index.html#org.apache.spark.mllib.recommendation.ALS) for details on the API. +Refer to the [`ALS` Scala docs](api/scala/index.html#org.apache.spark.mllib.recommendation.ALS) 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 (e.g., it is inferred from +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 %} @@ -85,7 +85,7 @@ Spark Java API uses a separate `JavaRDD` class. You can convert a Java RDD to a calling `.rdd()` on your `JavaRDD` object. A self-contained application example that is equivalent to the provided example in Scala is given below: -Refer to the [`ALS` Java docs](api/java/org/apache/spark/mllib/recommendation/ALS.html) for details on the API. +Refer to the [`ALS` Java docs](api/java/org/apache/spark/mllib/recommendation/ALS.html) for more details on the API. {% include_example java/org/apache/spark/examples/mllib/JavaRecommendationExample.java %} </div> @@ -99,7 +99,7 @@ Refer to the [`ALS` Python docs](api/python/pyspark.mllib.html#pyspark.mllib.rec {% 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 +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 %} |