People Search Digest

Traditional search engines use complex algorithms to determine search results. Such algorithms are cleverly designed and do a pretty good job of returning relevant results, but they can also give throw everything at you but the kitchen sink. And, sometimes, even the kitchen sink. Most people will be able to eventually find what they’re looking for, but you’ll have to wade through a lot of extraneous content.

A number of alternative search engines operate under entirely different methodologies. They range from analyzing a user’s Internet history to determine relevant results, to employing a user ranking system that measures the relevance of different results. So while complex algorithms still exist in social search engines, they do not produce static, uniform results; on the contrary, social search indexes always evolve as the community of users responds and reacts to the results.

The latest player to try its hand in the social search arena is London-based Nsyght. Though a full public launch is still weeks away, Nsyght has released a public beta featuring some interesting search analytics. It promises to evaluate search results based on a user’s bookmarks, social networks, and online friend profiles, using “Social Network Portability” to allow imported user and friend data from digg, last.fm, twitter, and pownce. This new beta has a clean, elegant design and several cool features, like Tag support, but it’s still pretty barebones and geek-oriented.

Nsyght’s success rests on its premise that the user community can do a better job determining content relevancy than any algorithm could. It still remains to be seen if Nsyght can be a major player in the social search world, but if the beta is any indication, we think it’ll do just fine.