Expertise retrieval in bibliographic network

Hashemi, Seyyed Hadi; Neshati, Mahmood; Beigy, Hamid

doi:10.1145/2505515.2505697

Cited by 28 publications

(13 citation statements)

References 21 publications

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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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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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show abstract

“…[5], [18], [23], [45], [53], [54]), and the topic-oriented approaches (cf. [10], [11], [13], [14], [24], [27], [28], [35], [40], [41], [42], [47], [48]). …”

Section: Expert Findingmentioning

confidence: 99%

“…Deng et al [11], Mimno and McCallum [28] and Hashemi et al [14] develop latent user model for the problem of expert finding in DBLP bibliography. Guo et al [13] and Zhou et al [48] introduce the topic sensitive probabilistic approach to build the latent user model.…”

Section: Expert Findingmentioning

confidence: 99%

Expert Finding for Question Answering via Graph Regularized Matrix Completion

Zhao

Zhang

et al. 2015

IEEE Trans. Knowl. Data Eng.

154

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Abstract-Expert finding for question answering is a challenging problem in community-based question answering (CQA) systems, arising in many real applications such as question routing and identification of best answers. In order to provide high-quality experts, many existing approaches learn the user model from their past question-answering activities in CQA systems. However, the past activities of users in most CQA systems are rather few, and thus the user model may not be well inferred in practice. In this paper, we consider the problem of expert finding from the viewpoint of missing value estimation. We then employ users' social networks for inferring user model, and thus improve the performance of expert finding in CQA systems. In addition, we develop a novel graph-regularized matrix completion algorithm for inferring the user model. We further develop two efficient iterative procedures, GRMC-EGM and GRMC-AGM, to solve the optimization problem. GRMC-EGM utilizes the Extended Gradient Method (EGM), while GRMC-AGM applies the Accelerated proximal Gradient search Method (AGM), for the optimization. We evaluate our methods on the well-known question answering system Quora, and the popular social network Twitter. Our empirical study shows the effectiveness of the proposed algorithms in comparison to the state-of-the-art expert finding algorithms.

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“…The predictive feature is varied in different link prediction problems, e.g. 1) adopts the publish sequence, topic and language model to find out the domain experts [10,5,4]; 2) considered the organizational overlap from the SNS (Social Network Service) user profile to predict the social relation [11,12].…”

Section: Related Workmentioning

confidence: 99%

Tracing and Predicting Collaboration for Junior Scholars

Tsai

Lin

2016

Proceedings of the 25th International Conference Companion on World Wide Web - WWW '16 Companion

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Academic publication is a key indicator for measuring scholars' scientific productivity and has a crucial impact on their future career. Previous work has identified the positive association between the number of collaborators and academic productivity, which motivates the problem of tracing and predicting potential collaborators for junior scholars. Nevertheless, the insufficient publication record makes current approaches less effective for junior scholars. In this paper, we present an exploratory study of predicting junior scholars' future co-authorship in three different network density. By combining features based on affiliation, geographic and content information, the proposed model significantly outperforms the baseline methods by 12% in terms of sensitivity. Furthermore, the experiment result shows the association between network density and feature selection strategy. Our study sheds light on the re-evaluation of existing approaches to connect scholars in the emerging worldwide Web of Scholars.

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Expertise retrieval in bibliographic network

Cited by 28 publications

References 21 publications

A Survey on Expert Recommendation in Community Question Answering

A Survey on Expert Recommendation in Community Question Answering

Expert Finding for Question Answering via Graph Regularized Matrix Completion

Tracing and Predicting Collaboration for Junior Scholars

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