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From BenFradet <>
Subject [GitHub] spark pull request: [SPARK-12247] [ML] [DOC] Documentation for spa...
Date Sat, 13 Feb 2016 17:18:40 GMT
Github user BenFradet commented on a diff in the pull request:
    --- Diff: docs/ ---
    @@ -0,0 +1,147 @@
    +layout: global
    +title: Collaborative Filtering -
    +displayTitle: Collaborative Filtering -
    +* Table of contents
    +## 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.  `` 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.
    +`` uses the [alternating least squares
    +algorithm to learn these latent factors. The implementation in `` 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
    +  *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
    +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
    --- End diff --
    @srowen tried to take your remarks into account, I don't know if it's clearer now though.

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