The task of expert finding has been getting increasing attention in information retrieval literature. However, the current state-of-the-art is still lacking in principled approaches for combining different sources of evidence. This paper explores the usage of unsupervised rank aggregation methods as a principled approach for combining multiple estimators of expertise, derived from the textual contents, from the graph-structure of the citation patterns for the community of experts, and from profile information about the experts. We specifically experimented two unsupervised rank aggregation approaches well known in the information retrieval literature, namely CombSUM and CombMNZ. Experiments made over a dataset of academic publications for the area of Computer Science attest for the adequacy of these methods.
Fox and Shaw @cite_14 @cite_18 defined several rank aggregation techniques (e.g., CombSUM and CombMNZ) which have been the object of much IR research since, including in the area of expert search @cite_15 . In our experiments, we compared the CombSUM and CombMNZ unsupervised rank aggregation methods, which are detailed in Section 3.
@cite_18 @cite_14 @cite_15
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This paper is concerned with the problem of unsupervised rank aggregation in the context of metasearch in information retrieval. In such tasks, we are given many partial ordered lists of retrieved items provided by many search engines and we want to define a way for aggregating those lists in order to find out a consensus. One classical approach consists in aggregating, for each retrieved item, the scores given by the different search engines. Then, we use the resulting aggregated scores distribution in order to infer a consensus ordered list. In this paper we investigate whether aggregation operators defined in the fields of multi-sensor fusion and multicriteria decision making are of interest for metasearch problems or not. Moreover, another purpose of this paper is to introduce a new aggregation operator, its foundations and its properties. We finally test all these aggregation operators for metasearch tasks using the Letor 2.0 dataset. Our results show that among the studied aggregation functions, the ones which are more compensatory outperform the baseline methods CombSUM and CombMNZ. The TREC-2 project at Virginai Tech focused on methods for combining the evidence from multiple retrieval runs to improve performance over any single retrieval method. This paper describes one such method that has been shown to increase performance by combining the similarity values from five different retrieval runs using both vector space and P-norm extended boolean retrieval methods In an expert search task, the users’ need is to identify people who have relevant expertise to a topic of interest. An expert search system predicts and ranks the expertise of a set of candidate persons with respect to the users’ query. In this paper, we propose a novel approach for predicting and ranking candidate expertise with respect to a query, called the Voting Model for Expert Search. In the Voting Model, we see the problem of ranking experts as a voting problem. We model the voting problem using 12 various voting techniques, which are inspired from the data fusion field. We investigate the effectiveness of the Voting Model and the associated voting techniques across a range of document weighting models, in the context of the TREC 2005 and TREC 2006 Enterprise tracks. The evaluation results show that the voting paradigm is very effective, without using any query or collection-specific heuristics. Moreover, we show that improving the quality of the underlying document representation can significantly improve the retrieval performance of the voting techniques on an expert search task. In particular, we demonstrate that applying field-based weighting models improves the ranking of candidates. Finally, we demonstrate that the relative performance of the voting techniques for the proposed approach is stable on a given task regardless of the used weighting models, suggesting that some of the proposed voting techniques will always perform better than other voting techniques.