The results suggest that using a small pool of a couple hundred experts, possibly your own experts or experts selected and mined from the web, has quite a bit of value, especially in cases where big data from a large community is unavailable.
A brief excerpt from the paper:
Recommending items to users based on expert opinions .... addresses some of the shortcomings of traditional CF: data sparsity, scalability, noise in user feedback, privacy, and the cold-start problem .... [Our] method's performance is comparable to traditional CF algorithms, even when using an extremely small expert set .... [of] 169 experts.The authors certainly do not claim that using a small pool of experts is better than traditional collaborative filtering.
Our approach requires obtaining a set of ... experts ... [We] crawled the Rotten Tomatoes web site –- which aggregates the opinions of movie critics from various media sources -- to obtain expert ratings of the movies in the Netflix data set.
What they do say is that using a very small pool of experts works surprisingly well. In particular, I think it suggests a good alternative to content-based methods for bootstrapping a recommender system. If you can create a high quality pool of experts, even a fairly small one, you may have good results starting with that while you work to gather ratings from the broader community.