A Topic-Specific Contextual Expert Finding Method in Social Network

Xie, Xiaoqin; Li, Yijia; Zhang, Zhiqiang; Pan, Haiwei; Han, Shihao

doi:10.1007/978-3-319-45814-4_24

Cited by 8 publications

(16 citation statements)

References 15 publications

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“…Additionally, reference [25] proposed a context-aware researcher recommendation system based on combining content based, the social network-based and the sum method. In order to find experts for a certain topic, the authors of reference [88] combined topic-aware model (TAM) and context-aware model (CAM) to propose a topic-specific contextual expert finding method. Also, reference [53] combined social network analysis and semantic concept analysis to propose a network based researcher recommendation approach to enhance the efficiency of personalized researcher recommendation.…”

Section: Hybrid Methodsmentioning

confidence: 99%

“…It has been used by references [47,66]. Additionally reference, [88] combined CiteSeer and Twitter test collections. They extracted the title, abstract and citation relationship from CiteSeer and user ID, posts, reading and commenting information from Twitter.…”

Section: Rq5: What Are the Datasets That Have Been Used For Expert Fimentioning

confidence: 99%

“…There are different types of documents that have been used in the current expert finding systems studies. The most used textual expertise source is publications, which have been used by authors in [36,38,44,48,49,53,58,59,61,62,[66][67][68][70][71][72][73]80] as their main expertise source, while publications have been combined with other expertise sources in [22,40,41,43,44,47,55,60,69,75,88,89]. In addition to publications, reference [42] used movies as expertise evidence.…”

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confidence: 99%

“…These social factors included knowledge level, background, experience, and personal preferences of the experts. Also, the authors of reference [88] combined experts' locations and time into their system. In academia, various factors were combined for developing expert finding systems.…”

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confidence: 99%

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Expert Finding Systems: A Systematic Review

et al. 2019

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The data overload problem and the specific nature of the experts’ knowledge can hinder many users from finding experts with the expertise they required. There are several expert finding systems, which aim to solve the data overload problem and often recommend experts who can fulfil the users’ information needs. This study conducted a Systematic Literature Review on the state-of-the-art expert finding systems and expertise seeking studies published between 2010 and 2019. We used a systematic process to select ninety-six articles, consisting of 57 journals, 34 conference proceedings, three book chapters, and one thesis. This study analyses the domains of expert finding systems, expertise sources, methods, and datasets. It also discusses the differences between expertise retrieval and seeking. Moreover, it identifies the contextual factors that have been combined into expert finding systems. Finally, it identifies five gaps in expert finding systems for future research. This review indicated that ≈65% of expert finding systems are used in the academic domain. This review forms a basis for future expert finding systems research.

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Section: Hybrid Methodsmentioning

confidence: 99%

Section: Rq5: What Are the Datasets That Have Been Used For Expert Fimentioning

confidence: 99%

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confidence: 99%

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confidence: 99%

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Expert Finding Systems: A Systematic Review

et al. 2019

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“…To ameliorate this drawback of the profile-based expert recommendation method, studies have been conducted on methods of recommending an expert by analyzing user activity details [29][30][31][32]. This method determines the similarity of a user's question by considering activities such as the user's wr recommended.…”

Section: Introductionmentioning

confidence: 99%

Expert Recommendation for Answering Questions on Social Media

et al. 2021

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In this paper, we propose a method for recommending experts to appropriately answer questions based on social activity analysis on social media. By analyzing various social activities performed on social media, the user’s interests are identified. Through the human relation analysis of the users of a particular interest field and by considering the response speed and answer quality of the user, we determine the influence of a user. An expert group is matched by analyzing the content of queries by a user and using a hierarchical structure of words. For a user question, the accuracy of an expert recommendation is enhanced by incorporating the question content and sublevel words based on the hierarchical structure of words. Various evaluations have demonstrated that the performance of the proposed method is superior to existing methods.

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Enhancing group recommender systems: A fusion of social tagging and collaborative filtering for cohesive recommendations

Wang,

Kamran,

Shahzad

et al. 2024

Syst Res Behav Sci

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This study examines the challenges and opportunities of using group recommendation systems in an information overload scenario. Social network recommendation systems are increasingly important because they deliver users customized choices. Most existing solutions are geared for single users, making it difficult to propose for a group with different interests. This paper analyses group recommendation systems and exposes their flaws. This study tested whether the suggested approach outperforms the one without tagging information in recall, precision, and user satisfaction. Empirical evidence indicates that the algorithm exhibits appropriate levels of reliability and accuracy compared to conventional methods. The proposed approach has the potential to substantially enhance the existing state of social network group recommendation systems, thereby facilitating users in their quest to identify and participate in groups that align with their preferences.

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A Topic-Specific Contextual Expert Finding Method in Social Network

Cited by 8 publications

References 15 publications

Expert Finding Systems: A Systematic Review

Expert Finding Systems: A Systematic Review

Expert Recommendation for Answering Questions on Social Media

Enhancing group recommender systems: A fusion of social tagging and collaborative filtering for cohesive recommendations

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