Article ID Journal Published Year Pages File Type
4966413 Information Processing & Management 2017 16 Pages PDF
Abstract
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 - NaïveRank, 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.
Related Topics
Physical Sciences and Engineering Computer Science Computer Science Applications
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