Author: pat
Date: Sun Mar 8 22:29:56 2015
New Revision: 1665091
URL: http://svn.apache.org/r1665091
Log:
CMS commit to mahout by pat
Modified:
mahout/site/mahout_cms/trunk/content/users/recommender/quickstart.mdtext
Modified:
mahout/site/mahout_cms/trunk/content/users/recommender/quickstart.mdtext
URL:
http://svn.apache.org/viewvc/mahout/site/mahout_cms/trunk/content/users/recommender/quickstart.mdtext?rev=1665091&r1=1665090&r2=1665091&view=diff
==============================================================================
--- mahout/site/mahout_cms/trunk/content/users/recommender/quickstart.mdtext
(original)
+++ mahout/site/mahout_cms/trunk/content/users/recommender/quickstart.mdtext
Sun Mar 8 22:29:56 2015
@@ -6,13 +6,13 @@ Recommenders have changed over the years
-To integrate with your application you will collect user interactions storing
them in a DB and also in a from usable by Mahout. The simplest way to do this
is log interactions to csv files (user-id, item-id). The DB should be setup to
contain the last n user interactions, which will form part of the query for
recommendations.
+To integrate with your application you will collect user interactions storing
them in a DB and also in a from usable by Mahout. The simplest way to do this
is to log user interactions to csv files (user-id, item-id). The DB should be
setup to contain the last n user interactions, which will form part of the
query for recommendations.
Mahout's spark-itemsimilarity will create a table of (item-id,
list-of-similar-items) in csv form. Think of this as an item collection with
one field containing the item-ids of similar items. Index this with your search
engine.
When your application needs recommendations for a specific person, get the
latest user history of interactions from the DB and query the indicator
collection with this history. You will get back an ordered list of item-ids.
These are your recommendations. You may wish to filter out any that the user
has already seen but that will depend on your use case.
-All ids for users and items are as 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.
+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
