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From: "David Parks" <davidparks21@yahoo.com>
To: <user@mahout.apache.org>
Subject: User and Item based recommender questions - real time updates &
 weighting similar items
Date: Fri, 28 Sep 2012 17:20:59 +0700
Message-ID: <0bbe01cd9d62$f66c2210$e3446630$@yahoo.com>
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I have two questions concerning User Based Recommenders and Item Based
Recommenders:

USER RECOMMENDER QUESTION:
In a User Based Recommender (in production after the model is computed), I
will receive a query for a User Based Recommendation that is based on newly
generated User Preference Data. 

>From my study so far it seems like I should try TreeClusteringRecommender to
start with, but how do I use the users most recent preference data to
generate a result in real time (within a single web transaction)? I need to
update the model in each query right? E.g.
myTreeClusteringRecommender.refresh()?

ITEM RECOMMENDER QUESTION:
In an Item Based Recommender I can call recommender.mostSimilar(itemIDs)
with a set of items that the user has expresses preference for (most recent
preference data). 

Is there a way I can weight these preferences? For example a user might have
already clicked on 2 items, and just looked at 3 others. If this is my
itemIDs set, the first two should affect the recommendation more than the
other 3.