An ant-colony based approach for real-time implicit collaborative information seeking

We propose an approach based on Swarm Intelligence - more specifically on Ant Colony Optimization (ACO) | to improve search engines' performance and reduce information overload by exploiting collective users' behavior. We designed and developed three different algorithms that employ an ACO-inspired strategy to provide implicit collaborative-seeking features in real time to search engines. The three different algorithms | Na veRank, RandomRank, and SessionRank | leverage on different principles of ACO in order to exploit users' interactions and provide them with more relevant results. We designed an evaluation experiment employing two widely used standard datasets of query-click logs issued to two major Web search engines. The results demonstrated how each algorithm is suitable to be employed in ranking results of different types of queries depending on users' intent.This work has been partially supported by ANTASTIC - a bio-inspired approach to social search interactions. YGGDRASIL: the Research Council of Norway Grants for highly quali ed, younger researchers (2013). Project n. 220050/F11. We would like to acknowledge the Research Visibility Award funded by Brunel University London that allowed the main author to establish a research network on collaborative information seeking with the co- authors of this wor