Combining CORI and the Decision-Theoretic Approach for Advanced Resource Selection

Abstract

AbstractIn this paper we combine two existing resource selection approaches, CORI and the decision-theoretic framework (DTF). The state-of-the-art system CORI belongs to the large group of heuristic resource ranking methods which select a fixed number of libraries with respect to their similarity to the query. In contrast, DTF computes an optimum resource selection with respect to overall costs (from different sources, e.g. retrieval quality, time, money). In this paper, we improve CORI by integrating it with DTF: The number of relevant documents is approximated by applying a linear or a logistic function on the CORI library scores. Based on this value, one of the existing DTF variants (employing a recall-precision function) estimates the number of relevant documents in the result set. Our evaluation shows that precision in the top ranks of this technique is higher than for the existing resource selection methods for long queries and lower for short queries; on average the combined approach outperforms CORI and the other DTF variants.KeywordsRelevant DocumentAverage PrecisionQuery TermResource SelectionShort QueryThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.