Understanding expert finding systems: domains and techniques

Al-Taie, Mohammed Zuhair; Kadry, Seifedine; Obasa, Adekunle Isiaka

doi:10.1007/s13278-018-0534-x

Cited by 19 publications

(11 citation statements)

References 55 publications

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“…Proposed study applies feature-based prediction and social network-based predictions. e authors of [24] have proposed a method of nding expert based on the theme in query likelihood language (QLL). In this method, signi cance values are assigned to the words in the query that is based on their capabilities of representing a theme.…”

Section: Related Workmentioning

confidence: 99%

Combining Latent Factor Model for Dynamic Recommendations in Community Question Answering Forums

Usman

Ahmad

Habib

et al. 2022

Computational Intelligence and Neuroscience

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Community Question Answering (CQA) web service provides a platform for people to share knowledge. Quora, Stack Overflow, and Yahoo! Answers are few sites where questioners post their queries and answerers respond to their respective queries. Due to the ease of use and quick responsiveness of the CQA platform, these sites are being widely adopted by the community. For better usability, there is a dire need to route the question toward the relevant answerers. To fulfil this gap, recommender systems play an important role in identifying the relevant answerers. To map the user interests more effectively, this research work proposed a dynamic feature representation of the latent user attributes for user profiling. The latent features are mapped by leveraging the Latent Dirichlet Allocation (LDA) for topic modelling of user data. The proposed recommendation model segments the user profile based on these latent user profiles incorporating the incremental learning of the users’ interests to produce the relevant recommendations in near real time. The experimental setup generated recommendation lists of variable sizes and evaluated using multiple evaluation metrics, such as mean average precision, recall, throughput, and different quality metrics, such as discounted cumulative gain and mean reciprocal rank. The results showed that the proposed model provided a better quality of recommendations in CQA forums, which is promising for future research in this domain.

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Section: Related Workmentioning

confidence: 99%

Combining Latent Factor Model for Dynamic Recommendations in Community Question Answering Forums

Usman

Ahmad

Habib

et al. 2022

Computational Intelligence and Neuroscience

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“…The aim of expert finding methods (also referred to as expert recommendation, expert search, or expert identification) is to find experts in specific areas. A sign of the increasing interest in these methods is the existence of recent reviews on the subject [2,29,40,50,57].…”

Section: Related Workmentioning

confidence: 99%

“…Expert finding methods have many applications: assigning appropriate reviewers to papers submitted for a conference or journal [37], detecting potential answerers in community question answering (CQA) systems [44,50,49], finding collaborators for a project [2], identifying suitable experts [21] in the academic world [30,43], social media [8,52], companies, institutions and organizations [5,25] or the whole web [19]. Other potential applications of expert finding are explained in [40].…”

Section: Related Workmentioning

confidence: 99%

“…The objective of this paper is therefore to determine if LDA is a good method for building expert subprofiles in the term domain in the context of political expert finding. The main contributions of this paper are: (1) we study the use of LDA to generate multiple subprofiles of terms, thematically homogeneous, for expert finding in a political setting; (2) we propose a method to divide documents into homogeneous subdocuments by distributing the appearances of each term in the document, using the matrices generated by LDA from the document collection;…”

Section: Introductionmentioning

confidence: 99%

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LDA-based term profiles for expert finding in a political setting

et al. 2021

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Internet and Web technologies have changed our lives in ways we are not yet fully aware of. In the near future, Internet will interconnect more than fifty billion things in the real world, nodes will sense billions of features and properties of interest, and things will be represented by web-based, bi-directional services with highly dynamic content and real-time data. This is the new era of the Internet and the Web of Things. Since the emergence of such paradigms implies the evolution and integration of the systems with which they interact, it is essential to develop abstract models for representing and simulating the Web of Things in order to establish new approaches. This paper describes a Web of Things model based on a structured XML representation. We also present a simulator whose ultimate goal is to encapsulate the expected dynamics of the Web of Things for the future development of information retrieval (IR) systems. The simulator generates a real-time collection of XML documents containing spatio-temporal contexts and textual and sensed information of highly dynamic dimensions. The simulator is characterized by its flexibility and versatility for representing real-world scenarios and offers a unique perspective for information retrieval. In this paper, we evaluate and test the simulator in terms of its performance variables for computing resource consumption and present our experimentation with the simulator on three real scenarios by considering the generation variables for the IR document collection.

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“…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. To facilitate the academic progress of the issue, Al‐Taie, Kadry, and Obasa (2018), Wang, Huang, Yao, Benatallah, and Dong (2018) and Yuan, Zhang, Tang, Hall, and Cabotà (2020) concluded the taxonomy and the state‐of‐the‐art of expert finding, and pointed out the possible direction for further research.…”

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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Understanding expert finding systems: domains and techniques

Cited by 19 publications

References 55 publications

Combining Latent Factor Model for Dynamic Recommendations in Community Question Answering Forums

Combining Latent Factor Model for Dynamic Recommendations in Community Question Answering Forums

LDA-based term profiles for expert finding in a political setting

Domain problem‐solving expert identification in community question answering

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