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From "David Parks" <davidpark...@yahoo.com>
Subject User and Item based recommender questions - real time updates & weighting similar items
Date Fri, 28 Sep 2012 10:20:59 GMT
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.


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