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From Jeffrey <>
Subject Needs clue to create a Proof of Concept recommender
Date Tue, 09 Aug 2011 06:27:22 GMT

I am trying to implement a recommender system for my postgraduate project. I currently have
all my data (collected using flickr API) stored in the MySQL database in RDF form using Redland
<> (lol, PHP is my main language hence Redland).

The recommender system is basically designed similarly with the paper published by Jonathan
Gemmell et. al (reference listed below), where tag clusters are also generated to find out
the similarity measure between clusters and items/users (hence was really frustrating when
I failed to dump the points for fuzzy k-means cluster). I am currently reading some articles
on implementing taste (recommender framework) with mahout but the use cases described in the
article are quite different than what I am about to implement.

I am still trying to build the tag clusters properly now. Each tag is now represented as a
vector of resources (each equivalent to a row in item-tag matrix), I am currently generate
the vector by converting a pre-generated arff by following this tutorial <>.
Is there another way of doing this (is it possible to generate the vectors without first generate
arff)? I have also read this <>
but can't seem to relate it to my use case right now.

Since I can't dump the points for the clusters using cluster dumper (keep getting OME) I
would probably calculate the degree of membership manually. Where should I store the result
(MySQL via JDBC? Hadoop Bigtable? Cassandra?) so that I can reuse it later for further calculation
(eg. similarity of an item with a cluster)?

Shepitsen, Andriy; Gemmell, Jonathan; Mobasher, Bamshad; Burke Robin. Personalized Recommendation
in Folksonomies. Proceedings of the 2nd International Conference on Recommender Systems. Lausanne,
Switzerland. October 23, 2008. 

p/s: I probably really should find a copy of "Mahout in Action" since I keep seeing it being

best wishes,

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