Googlers Beverly Yang and Gleh Jeh presented a paper, "Retroactive Answering of Search Queries" (PDF), at WWW 2006 that discusses an interesting idea around recommendations for web search.
The authors built a prototype that "retroactively answers queries from a user's history as new results arise." It is a little like automatically constructing Google Alerts for searchers and informing each person of any interesting, new results. Yang and Jeh say that they want to "focus on and address known, specific user needs" by "making web page recommendations corresponding to specific past queries."
The system itself is pretty straightforward. "Standing interests" are determined by queries that have a lot of activity (e.g. > 8 clicks on search results, > 3 refinements of the query, and repeated searches on the query). For each standing interest, the prototype tries to find new results with high PageRank for the query, then presents those results as recommendations.
One interesting result from the quick user study described in the paper was that rank -- how high the search result appears on the search result page -- was inversely correlated with perceived recommendation quality.
I think this may be best explained by people wanting the recommendations to help them discover things they would not have found on their own. The best recommendations are new, surprising, and useful. If I can find it easily myself because it shows up high on search results, that is not new, surprising, or useful.
The paper does not explore using data about what other people have found for the recommendations. That is unfortunate. In my experience, the most surprising and useful recommendations come from other people.
The paper does focus quite a bit on the problem of identifying these "standing interests", queries that a particular user does again and again. This seems similar to building a user subject interest profile (e.g. I like geeky computer and business stuff), but much more specific. As the authors briefly point out, the "automatic identification of standing interests in the form of specific queries can be especially valuable in ads targeting."