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Iterative Refinement Methods for Enhanced Information Retrieval
Author(s) -
Zhou Dong,
Truran Mark,
Liu Jianxun,
Li Wei,
Jones Gareth
Publication year - 2014
Publication title -
international journal of intelligent systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.291
H-Index - 87
eISSN - 1098-111X
pISSN - 0884-8173
DOI - 10.1002/int.21641
Subject(s) - computer science , information retrieval , search engine , exploit , collaborative filtering , human–computer information retrieval , baseline (sea) , iterative method , data mining , iterative refinement , recommender system , algorithm , oceanography , computer security , geology
Information retrieval (IR) systems exploit relevant information when tailoring search results to individual information needs. However, current search experience becomes poor without considering similar queries entered by previous searchers. In the following paper, we discuss a solution to this problem, which combines collaborative filtering algorithms with traditional IR models to enable EIR . We also present various iterative refinement methods for improving the raw performance of this system. We validate our theories in an experiment using queries extracted from the click‐through log of a commercial search engine. According to our results, an IR system employing iteratively refined, collaborative retrieval significantly outperforms various baseline retrieval models.