The post picks on the root mean squared error (RMSE) measure used for evaluating recommender systems in the Netflix Prize, talks about precision and why making recommendations is like search, and discusses some factors that impact people's perception of the usefulness and quality of the recommendations that are not captured by RMSE.
If you have thoughts on evaluating recommender systems, please go to the article and comment there. I left many questions unanswered in that post and was hoping to get a bit of a discussion going over on that new CACM blog.
The blog@CACM is just getting started, but the list of contributors is quite impressive and includes Peter Norvig, Daniel Reed, Michael Stonebraker, and many others. If you like, you can get the feed here.
By the way, if you are an ACM member or just remember it fondly from school, you might also go check out the new CACM website. It's been recently redesigned to emphasize news articles on the front page and in its many new feeds.
[Full disclosure: I'm on the CACM web board.]
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