Community-Based Scholar Recommendation Modeling in Academic Social Network Sites

Chen, Jiemin; Tang, Yong; Li, Jianguo; Mao, Chengjie; Xiao, Jing

doi:10.1007/978-3-642-54370-8_28

Cited by 9 publications

(3 citation statements)

References 13 publications

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“…But the process of extracting the occurrence and position of each reference can be very time-consuming. Similar to citation network, Jiemin Chen et al put forward a community-based scholar commendation model [5], which needs constructing research-fields-based graphs firstly, detecting communities in the graphs and then making scholar recommendation by calculating friendship scores. The experimental results demonstrated that the approach outperforms the content-based user recommendation method .…”

Section: Citation Network-based Recommendation Methodsmentioning

confidence: 99%

A novel hybrid publication recommendation system using compound information

Yang

Liu

et al. 2019

World Wide Web

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Publication recommendation is an interesting but challenging research problem. Most existing studies only use partial information of papers' contents, reference network or co-author relationship, which leads to an unsatisfied recommendation result. In this study, we propose a novel hybrid publication recommendation approach using compound information which retrieves top-K most relevant papers from a publication depository for a set of user

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Section: Citation Network-based Recommendation Methodsmentioning

confidence: 99%

A novel hybrid publication recommendation system using compound information

Yang

Liu

et al. 2019

World Wide Web

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“…There are some previous works that are similar to our study. For example, in [29], a research-fields-based graph in ASNs was built, and a community-based scholar recommendation model was proposed [30]. The previous study was extended by processing the problem of subset community through the GraphChi framework in parallel and recommending the scholars within the community according to the relevant recommendation rules [10].…”

Section: Scholar Recommendationmentioning

confidence: 99%

Graph Embedding for Scholar Recommendation in Academic Social Networks

Yuan

Lin

et al. 2021

Front. Phys.

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The academic social networks (ASNs) play an important role in promoting scientific collaboration and innovation in academic society. Accompanying the tremendous growth of scholarly big data, finding suitable scholars on ASNs for collaboration has become more difficult. Different from friend recommendation in conventional social networks, scholar recommendation in ASNs usually involves different academic entities (e.g., scholars, scientific publications, and status updates) and various relationships (e.g., collaboration relationship between team members, citations, and co-authorships), which forms a complex heterogeneous academic network. Our goal is to recommend potential similar scholars for users in ASNs. In this article, we propose to design a graph embedding-based scholar recommendation system by leveraging academic auxiliary information. First, we construct enhanced ASNs by integrating two types of academic features extracted from scholars’ academic information with original network topology. Then, the refined feature representations of the scholars are obtained by a graph embedding framework, which helps the system measure the similarity between scholars based on their representation vectors. Finally, the system generates potential similar scholars for users in ASNs for the final recommendation. We evaluate the effectiveness of our model on five real-world datasets: SCHOLAT, Zhihu, APS, Yelp and Gowalla. The experimental results demonstrate that our model is effective and achieves promising improvements than the other competitive baselines.

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“…In its 2014 survey, Nature (Van Noorden, 2014: 127) reported that:The most-selected activity on both ResearchGate and Academia.edu was simply maintaining a profile in case someone wanted to get in touch – suggesting that many researchers regard their profiles as a way to boost their professional presence online. After that, the most popular options involved posting content related to work, discovering related peers, tracking metrics and finding recommended research papers. Chen et al (2014: 325) have suggested that ‘Large amounts of users hope to make friends with other users for potential academic collaborations in ASNSs’. Like Van Noorden, Levy et al (2016: 63) have highlighted the use of ASNS for ‘career development, paper distribution and short-term interactions’ rather than for long-term social interactions.…”

Section: Related Researchmentioning

confidence: 99%

Usage of academic social networking sites by Karachi social science faculty

Ali

Richardson

2017

IFLA Journal

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The last decade has seen the emergence of academic social networking sites as a vehicle for scholars to promote their research and communicate with other scholars in their field. Given the small number of studies on the use of such sites by Pakistani academics, the authors conducted an exploratory study of social science faculty members at five Karachi (Pakistan) public sector universities. Analysis of the 68 valid responses revealed that the primary reason for accessing an academic social networking site was to search for articles on the site. Results also showed that accruing citations was the main reason for which respondents uploaded their own publications. The findings validate a role for librarians to support academics in their creation of effective online academic profiles.

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Community-Based Scholar Recommendation Modeling in Academic Social Network Sites

Cited by 9 publications

References 13 publications

A novel hybrid publication recommendation system using compound information

A novel hybrid publication recommendation system using compound information

Graph Embedding for Scholar Recommendation in Academic Social Networks

Usage of academic social networking sites by Karachi social science faculty

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