Greg Linden has another insightful post on his blog about Recommender Systems. He argues that the systems can be tuned to recommend diversity (ala-Netflix), rather than the more too-similar echo chamber of stuff you see sometimes on Amazon.
Jeremy Liew at LightSpeed VCP had a good post recently about search query understanding being the future direction of search.
In my mind, recommender systems are part of that vision. A truly great search engine will seek to understand your queries, your query history, personal interests and recommend content.. rather than just give you a keyword-filtered & ranked slice of the web.
Yet there are other ways to achieve that kind of output. Search engines and AI in general are a good distance away from real query understanding (it requires some form of machine reading). If instead we consider bootstrapping a recommender system that is driven by people's recommendations on a topic.. we can potentially get there quicker. This is how you train product recommender systems (with purchase history).
A system that implicitly follows you around the web and allows your content to be communally shared into an index would at a minimum be a very fresh index of what people are looking at now. Combining this index with a social network of people (enabling matching of topically relevant users to you) and we have something of a human-filter of the web driving a content recommender.
Yes, this is what many social URL sharing sites are building now... but do they have the pieces all together to drive people to directed content rather than allowing them to surf the wave of current topics?