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 in an optimal way. This paper explores the usage of learning to rank methods as a principled approach for combining multiple estimators of expertise, derived from the textual contents, from the graph-structure with the citation patterns for the community of experts, and from profile information about the experts. Experiments made over a dataset of academic publications, for the area of Computer Science, attest for the adequacy of the proposed approaches.
Serdyukov and Macdonald have surveyed the most important concepts and representative previous works in the expert finding task @cite_5 @cite_16 . Two of the most popular and well-performing types of methods are the profile-centric and the document-centric approaches @cite_14 @cite_19 . Profile-centric approaches build an expert profile as a pseudo document, by aggregating text segments relevant to the expert @cite_21 . These profiles of experts are latter indexed and used to support the search for experts on a topic. Document-centric approaches are typically based on traditional document retrieval techniques, using the documents directly. In a probabilistic approach to the problem, the first step is to estimate the conditional probability @math of the query topic @math given a document @math . Assuming that the terms co-occurring with an expert can be used to describe him, @math can be used to weight the co-occurrence evidence of experts with @math in documents. The conditional probability @math of an expert candidate @math given a query @math can then be estimated by aggregating all the evidences in all the documents where @math and @math co-occur. Experimental results show that document-centric approaches usually outperform profile-centric approaches @cite_19 .
@cite_14 @cite_21 @cite_19 @cite_5 @cite_16
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The goal of the enterprise track is to conduct experiments with enterprise data — intranet pages, email archives, document repositories — that reflect the experiences of users in real organisations, such that for example, an email ranking technique that is effective here would be a good choice for deployment in a real multi-user email search application. This involves both understanding user needs in enterprise search and development of appropriate IR techniques. The goal of the enterprise track is to conduct experiments with enterprise data — intranet pages, email archives, document repositories — that reflect the experiences of users in real organizations, such that for example, an email ranking technique that is effective here would be a good choice for deployment in a real multi-user email search application. This involves both understanding user needs in enterprise search and development of appropriate IR techniques. The enterprise track began in TREC 2005 as the successor to the web track, and this is reflected in the tasks and measures. While the track takes much of its inspiration from the web track, the foci are on search at the enterprise scale, incorporating non-web data and discovering relationships between entities in the organization. As a result, we have created the first test collections for multi-user email search and expert finding. This year the track has continued using the W3C collection, a crawl of the publicly available web of the World Wide Web Consortium performed in June 2004. This collection contains not only web pages but numerous mailing lists, technical documents and other kinds of data that represent the day-to-day operation of the W3C. Details of the collection may be found in the 2005 track overview (, 2005). Additionally, this year we began creating a repository of information derived from the collection by participants. This data is hosted alongside the W3C collection at NIST. There were two tasks this year, email discussion search and expert search, and both represent refinements of the tasks initially done in 2005. NIST developed topics and relevance judgments for the email discussion search task this year. For expert search, rather than relying on found data as last year, the track participants created the topics and relevance judgments. Twenty-five groups took part across the two tasks. The automatic search for knowledgeable people in the scope of an organization is a key function which makes modern Enterprise search systems commercially successful and socially demanded. A number of effective approaches to expert finding were recently proposed in academic publications. Although, most of them use reasonably defined measures of personal expertise, they often limit themselves to rather unrealistic and sometimes oversimplified principles. In this thesis, we explore several ways to go beyond state-of-the-art assumptions used in research on expert finding and propose several novel solutions for this and related tasks. First, we describe measures of expertise that do not assume independent occurrence of terms and persons in a document what makes them perform better than the measures based on independence of all entities in a document. One of these measures makes persons central to the process of terms generation in a document. Another one assumes that the position of the person’s mention in a document with respect to the positions of query terms indicates the relation of the person to the document’s relevant content. Second, we find the ways to use not only direct expertise evidence for a person concentrated within the document space of the person’s current employer and only within those organizational documents that mention the person. We successfully utilize the predicting potential of additional indirect expertise evidence publicly available on the Web and in the organizational documents implicitly related to a person. Finally, besides the expert finding methods we proposed, we also demonstrate solutions for the tasks from related domains. In one case, we use several algorithms of multi-step relevance propagation to search for typed entities in Wikipedia. In another case, we suggest generic methods for placing photos uploaded to Flickr on the World map using language models of locations built entirely on the annotations provided by users with a few task specific extensions. 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.