An ant-colony based approach for real-time implicit collaborative information seeking. Issue 3 (May 2017)

• Record Type: Journal Article
• Authors: Malizia, Alessio; Olsen, Kai A.; Turchi, Tommaso; Crescenzi, Pierluigi
• Is Part Of: Information processing & management. Volume 53:Issue 3(2017:May)
• Page Start: 608
• Page End: 623
• Publication Date: 2017-05
• Subjects: Ant Colony Optimization -- Cooperative systems -- Evolutionary computation -- Information filtering -- Information retrieval -- Recommender systems -- World wide web; Information storage and retrieval systems -- Periodicals; Information science -- Periodicals; Systèmes d'information -- Périodiques; Sciences de l'information -- Périodiques; Information science; Information storage and retrieval systems; Periodicals; 658.4038
• DOI: 10.1016/j.ipm.2016.12.005
• Languages: English
• ISSNs: 0306-4573

Abstract

Highlights: Replaced the figures in the results section (pg. 26–27) presenting our results with two tables. Justified why using nDCG instead of hypothesis testing and statistical significance. 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.