One suggestion from today's social search session at #swfoo was to send queries off to both search engines and your friends (e.g., "vacations in Venice"). A problem here is that many of your friends are incompetent about vacations in Venice, so sending them this both spams them and decreases results relevancy -- noise increases linearly with overall size of system. This is why the good results that early adopters with 20K followers have with "what's the best pizza in Sebastopol" aren't scalable.
But, there's a nice solution to this I think. As you do get results that are somewhat relevant from friends, you click through on their answers. Your clicks tell the system that friend's answer was relevant in context, allowing it to learn which friends are competent in various fields. Combine these results across everyone who is asking questions of the same friends to cancel out bias; you're left with a vector of weights for each person in the network, one weight per field of expertise. Use this to do a few things:
- Explicit reputation for people who answer, to accompany the implicit social debt incurred
- Rank their answers higher in search results -- in many cases beating out traditional search engines if they're proved to be less competent
- Don't spam incompetent people with questions they can't answer
- Potentially, reach beyond your immediate social network to find the real experts on the subjects and send your question to them.
This is much more scalable than trying to categorize your friends explicitly as experts in various areas. You'll still do this implicitly, by first clicking on results from friends you already know to be expert, helping to bootstrap the system. But you never need to know you're doing this; the system learns automatically.