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.
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Iterative Refinement Methods for Enhanced Information Retrieval
Semantic Scholar · Computer Science · 2014
Abstract
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.