Expert2Vec: Experts Representation in Community Question Answering for Question Routing

Mumtaz, Sara; Rodríguez, Carlos; Benatallah, Boualem

doi:10.1007/978-3-030-21290-2_14

Cited by 18 publications

(14 citation statements)

References 18 publications

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“…In a recent study, Dehghan, Biabani, and Abin (2019) categorized experts into different types and presented an expertise tree structure to identify the expertise type of users. Mumtaz, Rodriguez, and Benatallah (2019) made an attempt to capture domain‐specific semantics that matches user expertise with question content by using the state‐of‐the‐art embedding word technique. Li, Jiang, Sun, and Wang (2019) developed a heterogeneous information network embedding algorithms and a novel Long Short‐Term Memory model to embed the information of question text, raiser and answerer into a unified representation.…”

Section: Related Workmentioning

confidence: 99%

Domain problem‐solving expert identification in community question answering

Tang

et al. 2020

Expert Systems

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Question-Answering (Q&A) services provide internet users with platforms to exchange knowledge and ideas. The development of Q&A sites, or Community Question Answering (CQA), mainly depends on the high-quality content continuously contributed by users with high-level expertise, who can be recognized as experts. Expert finding is an important task for the authorities of Q&A communities to encourage commitment. In a highly competitive market environment, CQA managers have to take measures to retain and nurture users, especially superior contributors. However, current expertise scoring techniques adopted in CQA often give much credit to very active users and fail to identify real experts. This study aims to develop a robust and practical expert identification framework for Q&A communities, by combining welldesigned expertise scoring technique and probabilistic clustering model. With regard to expert identification, a numerical metric of users' expertise is developed as the optimal expert finding strategy, and a clustering algorithm based on Gaussian-Gamma mixture model (GGMM) is proposed to efficiently distinguish experts from nonexperts. In the experiments, the proposed method is applied to real-world datasets collected from subcommunities of Stack Exchange Q&A networks. Results obtained from comparative experiments show that our method achieves better performance than the state-of-the-art methods and demonstrate the effectiveness of the proposed framework. The analysis shows that the framework which combines the proposed expertise scoring technique and Gaussian-Gamma mixture clustering model is capable of detecting excellent domain problem-solving experts who exhibit both domain interest and expertise.

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Section: Related Workmentioning

confidence: 99%

Domain problem‐solving expert identification in community question answering

Tang

et al. 2020

Expert Systems

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“…The present work proposes a semantic matching topic modelmodel approach to question routing. The analysis of questionand-answer questions and, more specifically, the application of natural language processing as an integral part of machine learning has been explored in various ways [5]. For example, the mining of questions about design patterns has been pursued to develop a knowledge base about problem-prone patterns [6], the recognition of mentions to architecture-relevant and technologyrelated information was pursued with the purpose of knowledge structuring [7], and the construction of probabilistic models to capture the correlation between natural language textual descriptions and code snippets enabled the implementation of query systems supporting code retrieval and synthesis [8].…”

Section: Related Workmentioning

confidence: 99%

Understanding the social evolution of the Java community in Stack Overflow: A 10-year study of developer interactions

Blanco

Pérez-López

Fdez-Riverola

et al. 2020

Future Generation Computer Systems

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Today, Social Media is a key information source for a wide range of domains as a means to gain a better understanding of information flows and user communities. This work introduces a methodology combining machine learning and graph mining approaches to address relevant aspects of quality of service and user intrinsic motivation from the user's perspective. The focus of the present analysis is set on the social interactions among software developers via Stack Overflow. Over the last 10 years, software developers have become intensively involved in knowledge sharing and platforms such as Stack Overflow have accumulated a lot of development data and knowledge. The proposed methodology is applied to explore the social dynamics of the Java programming language community and bring forward relevant, non-trivial knowledge about developer interests, information flows and user engagement and reputation. The ultimate aim is to improve question preparation towards better question routing and voting outcome.

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“…Over the recent years, many research studies have been conducted to solve different challenging problems on CQA networks [1,2,5]. In CQA networks, documents are posted by registered users in the forms of questions and answers.…”

Section: Expert Finding In Cqa Networkmentioning

confidence: 99%

“…Also, there is an ability for the questioner to determine the best answer of its question in some CQAs. The huge source of users' generated information in CQA networks, have attracted attention of many researchers to define challenging and practical problems such as expert finding [1], question routing [2,3], question classification [4,5]. Among all problems, expert finding with the aim of detecting and ranking talented people in the subject of user's query is a well-studied problem in the field of Information Retrieval (IR).…”

Section: Introductionmentioning

confidence: 99%

Mining shape of expertise: A novel approach based on convolutional neural network

Dehghan

Rahmani

Abin

et al. 2020

Information Processing & Management

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Expert finding addresses the task of retrieving and ranking talented people on the subject of user query. It is a practical issue in the Community Question Answering networks. Recruiters looking for knowledgeable people for their job positions are the most important clients of expert finding systems. In addition to employee expertise, the cost of hiring new staff is another significant concern for organizations. An efficient solution to cope with this concern is to hire T-shaped experts that are cost-effective. In this study, we have proposed a new deep model for T-shaped experts finding based on Convolutional Neural Networks. The proposed model tries to match queries and users by extracting local and position-invariant features from their corresponding documents. In other words, it detects users' shape of expertise by learning patterns from documents of users and queries simultaneously. The proposed model contains two parallel CNN's that extract latent vectors of users and queries based on their corresponding documents and join them together in the last layer to match queries with users. Experiments on a large subset of Stack Overflow documents indicate the effectiveness of the proposed method against baselines in terms of NDCG, MRR, and ERR evaluation metrics.

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Expert2Vec: Experts Representation in Community Question Answering for Question Routing

Cited by 18 publications

References 18 publications

Domain problem‐solving expert identification in community question answering

Domain problem‐solving expert identification in community question answering

Understanding the social evolution of the Java community in Stack Overflow: A 10-year study of developer interactions

Mining shape of expertise: A novel approach based on convolutional neural network

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