A CDD-based formal model for expert finding

Fu, Yupeng; Xiang, Rongjing; Liu, Yiqun; Zhang, Min; Ma, Shaoping

doi:10.1145/1321440.1321566

Cited by 10 publications

(5 citation statements)

References 2 publications

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“…Experts are found by mining expertise from email communications in [9]. Profile-based models for expert finding on general documents are proposed in [11]. There is also some research in question answerer recommendation.…”

Section: Expert Recommendationmentioning

confidence: 99%

Finding expert users in community question answering

Riahi

Zolaktaf

Shafiei

et al. 2012

Proceedings of the 21st International Conference on World Wide Web

172

102

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Community Question Answering (CQA) websites provide a rapidly growing source of information in many areas. This rapid growth, while offering new opportunities, puts forward new challenges. In most CQA implementations there is little effort in directing new questions to the right group of experts. This means that experts are not provided with questions matching their expertise, and therefore new matching questions may be missed and not receive a proper answer. We focus on finding experts for a newly posted question. We investigate the suitability of two statistical topic models for solving this issue and compare these methods against more traditional Information Retrieval approaches. We show that for a dataset constructed from the Stackoverflow website, these topic models outperform other methods in retrieving a candidate set of best experts for a question. We also show that the Segmented Topic Model gives consistently better performance compared to the Latent Dirichlet Allocation Model.

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Section: Expert Recommendationmentioning

confidence: 99%

Finding expert users in community question answering

Riahi

Zolaktaf

Shafiei

et al. 2012

Proceedings of the 21st International Conference on World Wide Web

172

102

View full text Add to dashboard Cite

show abstract

“…As a result, some improved models have also been proposed to estimate relevance between profiles and queries, e.g. the CDD model [3], which calculates weight for each collected fragment at first and then scores each profile according to the weights of its fragments.…”

Section: A the Profile-based Methodsmentioning

confidence: 99%

IR-Based Expert Finding Using Filtered Collection

Jiang

2008

2008 4th International Conference on Wireless Communications, Networking and Mobile Computing

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Existing IR-based expert finding generally follows two methods, i.e. the profile-based method and the voting-based one. However, neither the expert-relevant data collected in the profilebased method nor the query-relevant data used for the votingbased method is completely accurate within the confines of current relevance ranking approaches. This problem has been rarely discussed, but impedes expert finding. On this issue, we provide a feasible solution, that is, the collection can be filtered to generate a subset of high-precision relevant data for further processing. In this paper, we propose two perspectives of filtering approaches, i.e. the query-centered perspective and the expert-centered one. For both perspectives, some specific strategies are also discussed and experimented under the CERC collection using the TMJAC model, a voting-based method. On such basis, the different preferences of two perspectives are revealed. Further, to examine the stability of filtering, we examine the filtering strategies using a profile-based method and also testify the effects under the W3C collection. In conclusion, the filtering we proposed is a universal approach of improving expert finding performance.

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“…A slight difference of the methods developed based on studying these datasets is that they most aim to rank and find the most best-skilled or authoritative users given an existing domain or topic instead of a new question. These datasets include co-authorship network [52;98;99] such as DBLP [100][101][102] , social networks [16;103;104] , microblogs [105][106][107] such as Twitter [51] , Email network [108][109][110] , Internet forums [41] , log data [111] , e-Learning platform [112] , Usenet newsgroups [7;8] , Google Groups [9] , general documents [113] , and enterprise documents [20;26;114] such as Enterprise track of TREC [115][116][117] .…”

Section: Non-cqa Datasetsmentioning

confidence: 99%

A Survey on Expert Recommendation in Community Question Answering

Wang

Huang

Yao

et al. 2018

J. Comput. Sci. Technol.

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Community question answering (CQA) represents the type of Web applications where people can exchange knowledge via asking and answering questions. One significant challenge of most real-world CQA systems is the lack of effective matching between questions and the potential good answerers, which adversely affects the efficient knowledge acquisition and circulation. On the one hand, a requester might experience many low-quality answers without receiving a quality response in a brief time; on the other hand, an answerer might face numerous new questions without being able to identify their questions of interest quickly. Under this situation, expert recommendation emerges as a promising technique to address the above issues. Instead of passively waiting for users to browse and find their questions of interest, an expert recommendation method raises the attention of users to the appropriate questions actively and promptly. The past few years have witnessed considerable efforts that address the expert recommendation problem from different perspectives. These methods all have their issues that need to be resolved before the advantages of expert recommendation can be fully embraced. In this survey, we first present an overview of the research efforts and state-of-the-art techniques for the expert recommendation in CQA. We next summarize and compare the existing methods concerning their advantages and shortcomings, followed by discussing the open issues and future research directions.

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A CDD-based formal model for expert finding

Cited by 10 publications

References 2 publications

Finding expert users in community question answering

Finding expert users in community question answering

IR-Based Expert Finding Using Filtered Collection

A Survey on Expert Recommendation in Community Question Answering

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