A post on Data Community DC discusses Why You Should Not Build a Recommendation Engine. The main point is that recommendation engines need a lot of data to work properly, and you're unlikely to have that when you start out.
I know the feeling. In a previous job I created a recommendation engine for a business communication system. It used tags on the content and user behaviour to infer the topics that the user was most likely to be interested in, and recommend content accordingly. Unfortunately, my testbed was my employer's own instance of the product, and the company was a start-up that was too small to need its own product. I never really got a handle on how well it worked.
This brings me to Emily. Emily isn't a product. It's a personal portfolio project. I had an idea for a recommendation system that would infer users' interests from content they posted in blogs, and recommend similar content. The problem is, the content it recommends comes from the other users, so at its current early stage of operation, it doesn't have much to recommend. The more people use it, the better it will become, but what's the incentive to be an early adopter?
What I seem to have at the moment is a recommendation engine that needs somebody to recommend it.