Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/13754
Title: An ant-colony based approach for real-time implicit collaborative information seeking
Authors: Malizia, A
Olsen, K
Tommaso, T
Crescenzi, P
Keywords: Evolutionary computation;Information filtering;Information retrieval;Recommender systems;World wide web;Ant colony optimization;Cooperative systems
Issue Date: 2017
Publisher: Advanced Institute of Convergence Information Technology Research Center
Citation: International Journal of Information Processing and Management, pp. 1-49, (2017)
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 | 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.
URI: http://bura.brunel.ac.uk/handle/2438/13754
ISSN: 2233-940X
Appears in Collections:Dept of Computer Science Research Papers

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