The challenges of building good recommender systems

Rich MacManus’ article on the 5 Problems of Recommender systems struck a chord with me this week.  In particular he points out the challenge of sparse data in bootstrapping a good recommender system, and that the companies that subjectively seem to have the best engines are also those with the largest volumes of data. 

http://www.readwriteweb.com/archives/5_problems_of_recommender_systems.php

One of the challenges in making a startup recommender technology compelling is how well it performs in the "cold start" situation, one of my current research interest, one approach for doing so is to exploit non-preference based associations with the recommendation content corpus, nicely exemplified by Gunawardana and Meek’s paper, Tied Boltzmann Machines for Cold Start Recommendations.

http://research.microsoft.com/apps/pubs/default.aspx?id=69521 

Those of you who have known me for some time will remember that I’ve always said Boltzmann Machines’ would one day be useful!

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