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Search engines are nowadays one of the most important entry points forInternet users and a central tool to solve most of their information needs.Still, there exist a substantial amount of users' searches which obtainunsatisfactory results. Needless to say, several lines of research aim toincrease the relevancy of the results users retrieve. In this paper the authorsframe this problem within the much broader (and older) one of informationoverload. They argue that users' dissatisfaction with search engines is acurrently common manifestation of such a problem, and propose a different anglefrom which to tackle with it. As it will be discussed, their approach sharesgoals with a current hot research topic (namely, learning to rank forinformation retrieval) but, unlike the techniques commonly applied in thatfield, their technique cannot be exactly considered machine learning and,additionally, it can be used to change the search engine's response inreal-time, driven by the users behavior. Their proposal adapts concepts fromSwarm Intelligence (in particular, Ant Algorithms) from an Information Foragingpoint of view. It will be shown that the technique is not only feasible, butalso an elegant solution to the stated problem; what's more, it achievespromising results, both increasing the performance of a major search engine forinformational queries, and substantially reducing the time users require toanswer complex information needs.

Making the road by searching - A search engine based on Swarm Information Foraging