p***@apache.org
2018-06-26 19:09:34 UTC
Repository: mahout
Updated Branches:
refs/heads/master 574ccc990 -> 5a1d85f59
NO-JIRA fix markdown and update intro to recommenders page
Project: http://git-wip-us.apache.org/repos/asf/mahout/repo
Commit: http://git-wip-us.apache.org/repos/asf/mahout/commit/5a1d85f5
Tree: http://git-wip-us.apache.org/repos/asf/mahout/tree/5a1d85f5
Diff: http://git-wip-us.apache.org/repos/asf/mahout/diff/5a1d85f5
Branch: refs/heads/master
Commit: 5a1d85f59f3503102298b410a0b81c2acb458a76
Parents: 574ccc9
Author: pferrel <***@occamsmachete.com>
Authored: Tue Jun 26 12:08:00 2018 -0700
Committer: pferrel <***@occamsmachete.com>
Committed: Tue Jun 26 12:08:00 2018 -0700
----------------------------------------------------------------------
website/users/algorithms/recommender-overview.md | 14 +++++++++++---
1 file changed, 11 insertions(+), 3 deletions(-)
----------------------------------------------------------------------
http://git-wip-us.apache.org/repos/asf/mahout/blob/5a1d85f5/website/users/algorithms/recommender-overview.md
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diff --git a/website/users/algorithms/recommender-overview.md b/website/users/algorithms/recommender-overview.md
index cd69730..1f37f2a 100644
--- a/website/users/algorithms/recommender-overview.md
+++ b/website/users/algorithms/recommender-overview.md
@@ -8,7 +8,15 @@ title: Recommender Quickstart
# Recommender Overview
-Recommenders have changed over the years. Mahout contains a long list of them, which you can still use. But to get the best out of our more modern aproach we'll need to think of the Recommender as a "model creation" component—supplied by Mahout's new spark-itemsimilarity job, and a "serving" component—supplied by a modern scalable search engine, like Solr.
+Recommenders have changed over the years. Mahout contains a long list of them, which you can still use. However in about 2013 there was a revolution in recommenders, which favored what we might call "Multimodal", meaning they could take in data of all sorts—basically anything we might think was an indicator of user taste. The new Samsara algorithm, called Correlated Cross-Occurrence (CCO) is just such a next gen recommender algorithm but Mahout-Samsara only implements the model building part. This can be integrated as the user see fit and the rest of this doc will explain how.
+
+## Turnkey Implementation
+
+If you are looking for an end-to-end OSS recommender based on the Mahout CCO algorithm have a look at [The Universal Recommender](https://github.com/actionml/universal-recommender), which is implemented using [Apache PredictionIO](http://predictionio.apache.org/). See instructions for [installation here](http://actionml.com/docs/pio_by_actionml). There is even an AWS AMI for convenience (this is a for-pay option)
+
+## Build Your Own Integration
+
+To get the most out of our more modern CCO algorithm we'll need to think of the Recommender as a "model creation" component—supplied by Mahout's new spark-itemsimilarity job, and a "serving" component—supplied by a modern scalable search engine, like Solr or Elasticsearch. Here we describe a loose integration that does not require using Mahout as a library, it uses Mahout's command line interface. This is clearly not the best but allows one to experiments and get a real recommender running easily.
)
@@ -20,7 +28,7 @@ When your application needs recommendations for a specific person, get the lates
All ids for users and items are preserved as string tokens and so work as an external key in DBs or as doc ids for search engines, they also work as tokens for search queries.
-##References
+## References
1. A free ebook, which talks about the general idea: [Practical Machine Learning](https://www.mapr.com/practical-machine-learning)
2. A slide deck, which talks about mixing actions or other indicators: [Creating a Multimodal Recommender with Mahout and a Search Engine](http://occamsmachete.com/ml/2014/10/07/creating-a-unified-recommender-with-mahout-and-a-search-engine/)
@@ -28,6 +36,6 @@ All ids for users and items are preserved as string tokens and so work as an ext
and [What's New in Recommenders: part #2](http://occamsmachete.com/ml/2014/09/09/mahout-on-spark-whats-new-in-recommenders-part-2/)
3. A post describing the loglikelihood ratio: [Surprise and Coinsidense](http://tdunning.blogspot.com/2008/03/surprise-and-coincidence.html) LLR is used to reduce noise in the data while keeping the calculations O(n) complexity.
-##Mahout Model Creation
+## Mahout Model Creation
See the page describing [*spark-itemsimilarity*](http://mahout.apache.org/users/recommender/intro-cooccurrence-spark.html) for more details.
\ No newline at end of file
Updated Branches:
refs/heads/master 574ccc990 -> 5a1d85f59
NO-JIRA fix markdown and update intro to recommenders page
Project: http://git-wip-us.apache.org/repos/asf/mahout/repo
Commit: http://git-wip-us.apache.org/repos/asf/mahout/commit/5a1d85f5
Tree: http://git-wip-us.apache.org/repos/asf/mahout/tree/5a1d85f5
Diff: http://git-wip-us.apache.org/repos/asf/mahout/diff/5a1d85f5
Branch: refs/heads/master
Commit: 5a1d85f59f3503102298b410a0b81c2acb458a76
Parents: 574ccc9
Author: pferrel <***@occamsmachete.com>
Authored: Tue Jun 26 12:08:00 2018 -0700
Committer: pferrel <***@occamsmachete.com>
Committed: Tue Jun 26 12:08:00 2018 -0700
----------------------------------------------------------------------
website/users/algorithms/recommender-overview.md | 14 +++++++++++---
1 file changed, 11 insertions(+), 3 deletions(-)
----------------------------------------------------------------------
http://git-wip-us.apache.org/repos/asf/mahout/blob/5a1d85f5/website/users/algorithms/recommender-overview.md
----------------------------------------------------------------------
diff --git a/website/users/algorithms/recommender-overview.md b/website/users/algorithms/recommender-overview.md
index cd69730..1f37f2a 100644
--- a/website/users/algorithms/recommender-overview.md
+++ b/website/users/algorithms/recommender-overview.md
@@ -8,7 +8,15 @@ title: Recommender Quickstart
# Recommender Overview
-Recommenders have changed over the years. Mahout contains a long list of them, which you can still use. But to get the best out of our more modern aproach we'll need to think of the Recommender as a "model creation" component—supplied by Mahout's new spark-itemsimilarity job, and a "serving" component—supplied by a modern scalable search engine, like Solr.
+Recommenders have changed over the years. Mahout contains a long list of them, which you can still use. However in about 2013 there was a revolution in recommenders, which favored what we might call "Multimodal", meaning they could take in data of all sorts—basically anything we might think was an indicator of user taste. The new Samsara algorithm, called Correlated Cross-Occurrence (CCO) is just such a next gen recommender algorithm but Mahout-Samsara only implements the model building part. This can be integrated as the user see fit and the rest of this doc will explain how.
+
+## Turnkey Implementation
+
+If you are looking for an end-to-end OSS recommender based on the Mahout CCO algorithm have a look at [The Universal Recommender](https://github.com/actionml/universal-recommender), which is implemented using [Apache PredictionIO](http://predictionio.apache.org/). See instructions for [installation here](http://actionml.com/docs/pio_by_actionml). There is even an AWS AMI for convenience (this is a for-pay option)
+
+## Build Your Own Integration
+
+To get the most out of our more modern CCO algorithm we'll need to think of the Recommender as a "model creation" component—supplied by Mahout's new spark-itemsimilarity job, and a "serving" component—supplied by a modern scalable search engine, like Solr or Elasticsearch. Here we describe a loose integration that does not require using Mahout as a library, it uses Mahout's command line interface. This is clearly not the best but allows one to experiments and get a real recommender running easily.
)@@ -20,7 +28,7 @@ When your application needs recommendations for a specific person, get the lates
All ids for users and items are preserved as string tokens and so work as an external key in DBs or as doc ids for search engines, they also work as tokens for search queries.
-##References
+## References
1. A free ebook, which talks about the general idea: [Practical Machine Learning](https://www.mapr.com/practical-machine-learning)
2. A slide deck, which talks about mixing actions or other indicators: [Creating a Multimodal Recommender with Mahout and a Search Engine](http://occamsmachete.com/ml/2014/10/07/creating-a-unified-recommender-with-mahout-and-a-search-engine/)
@@ -28,6 +36,6 @@ All ids for users and items are preserved as string tokens and so work as an ext
and [What's New in Recommenders: part #2](http://occamsmachete.com/ml/2014/09/09/mahout-on-spark-whats-new-in-recommenders-part-2/)
3. A post describing the loglikelihood ratio: [Surprise and Coinsidense](http://tdunning.blogspot.com/2008/03/surprise-and-coincidence.html) LLR is used to reduce noise in the data while keeping the calculations O(n) complexity.
-##Mahout Model Creation
+## Mahout Model Creation
See the page describing [*spark-itemsimilarity*](http://mahout.apache.org/users/recommender/intro-cooccurrence-spark.html) for more details.
\ No newline at end of file