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

作者：Malizia Alessio^*; Olsen Kai A; Turchi Tommaso; Crescenzi Pierluigi

来源：Information Processing & Management, 2017, 53(3): 608-623.

DOI：10.1016/j.ipm.2016.12.005

• Ant Colony Optimization
• Cooperative systems
• Evolutionary computation
• Information filtering
• Information retrieval
• Recommender systems
• World wide web

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 - NalveRank, 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.

• 出版日期2017-5