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https://issues.apache.org/jira/browse/MAHOUT-1288?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13747119#comment-13747119
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Pat Ferrel commented on MAHOUT-1288:
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Have the ingest, create similarity matrix and write to csv workflow running.
This creates the docs that will be indexed by Solr as described in the design
doc.
This also includes the oft-talked about cross-action recommender in the form of
a modified RecommenderJob. Based on the Mahout Item-Based Recommender
implemented on Hadoop it implements two new jobs, XRecommenderJob and
PreparePreferenceMatricesJob. These ultimately create all recs for all users
and can be used separate from Solr.
Both single and cross-action data ([B'B], [B'A], B, and A) is written to csv
files for indexing by Solr where recs can be returned by query to Solr.
All but the first ingest phase runs in hadoop.
https://github.com/pferrel/solr-recommender
Proceeding to integrate with Solr and create the query API.
> Create recommendation as search demo
> ------------------------------------
>
> Key: MAHOUT-1288
> URL: https://issues.apache.org/jira/browse/MAHOUT-1288
> Project: Mahout
> Issue Type: New Feature
> Reporter: Ted Dunning
>
> The basic idea is that a recommendation engine can be deployed by doing
> off-line analysis to find anomalously cooccurring items and then indexing
> these items in a search engine. These anomalous items are considered
> indicators which are indexed using an ordinary text search engine.
> Recommendations are generated by querying the search engine using recent
> behavior as a query. Recommendations can be combined with geo filtering and
> text queries.
> See http://bit.ly/18vbbaT for a living design document.
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