Recommending Scientific Collaboration Based on Topical, Authors and Venues Similarities

Alshareef, Abdulrhman M.; Alhamid, Mohammed F.; Saddik, Abdulmotaleb El

doi:10.1109/iri.2018.00016

Cited by 14 publications

(8 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The model was motivated by researchers' behavior: they typically want to maintain contact with authors they encounter in venues, cite expert authors in a specific research area, look for high-quality and successful academic venues, join conferences that are closely related to their research and cite articles from high-quality venues and publishers. The model is an extension of our previous works [16], [17] that proposed academic recommendations based on topical, author and venue similarities, and achieved notable recommendation results. Here, we explain how to build a citation network and personalize the recommendation model by finding the latent preferences in a citation network for a given query.…”

Section: A Overview Of the Recommendation Modelmentioning

confidence: 95%

Academic Venue Recommendations Based on Similarity Learning of an Extended Nearby Citation Network

2019

Self Cite

View full text Add to dashboard Cite

The rapidly increasing number of potential academic venues for research publication and commentary has made sourcing the venue that would best contribute to promoting effective scientific cooperation more challenging. In this paper, we propose a similarity learning approach to determine the most appropriate venue to publish an article. We first analyze the article metadata and cited articles to build the citation network matrices of the given article and then apply these to learn and build similarity matrices between academic objects (i.e., articles, authors, and venues) at an extended nearby article citation network. Using the formed matrices, we estimate a collaborative anticipation confidence score of a relationship between the venues in the extended network. For our empirical studies, we used an actual academic dataset to validate the efficiency of our approach and recommend an appropriate academic venue. The experimental results highlight the effectiveness of our proposed approach to optimize overall recommendation quality, compared with other baseline approaches.

show abstract

Section: A Overview Of the Recommendation Modelmentioning

confidence: 95%

Academic Venue Recommendations Based on Similarity Learning of an Extended Nearby Citation Network

2019

Self Cite

View full text Add to dashboard Cite

show abstract

“…This need has already been identified, and approaches have been made to address it. Alshareef et al (2018) have proposed an algorithm that can be used to recommend researchers to each other as potential collaboration partners based on their similarities as evidenced by previous publications. However, as we have pointed out before, this approach targets homophilous collaboration only and does not provide support for heterophilous collaborations in interdisciplinary scientific communities.…”

Section: Knowing About Others: Group Awarenessmentioning

confidence: 99%

“…It seems especially relevant to continue to consider the question regarding what information should be shown to participants as we can assume that the available information will have an impact on what selection patterns are emphasized. Specific recommendations of relevant researchers based on previous publications as suggested by Alshareef et al, (2018) may support homophilous selection. Specific recommendations based on current research interests as implemented by Windhager et al, (2014) may, in contrast, allow for more diverse selection and be especially helpful to participants with a very clear focus on what future collaboration they want to pursue.…”

Section: The Effect Of Group Awareness Support On Selection Patterns In the Initiation Of New Research Collaborationsmentioning

confidence: 99%

Initiating scientific collaborations across career levels and disciplines – a network analysis on behavioral data

Eberle

Stegmann

Barrat

et al. 2021

Intern. J. Comput.-Support. Collab. Learn

View full text Add to dashboard Cite

Collaborations are essential in research, especially in answering increasingly complex questions that require integrating knowledge from different disciplines and that engage multiple stakeholders. Fostering such collaboration between newcomers and established researchers helps keep scientific communities alive while opening the way to innovation. But this is a challenge for scientific communities, especially as little is known about the onset of such collaborations. Prior social network research suggests that face-to-face interaction at scientific events as well as both network-driven selection patterns (reciprocity and transitivity) and patterns of active selection of specific others (homophily / heterophily) may be important. Learning science research implies, moreover, that selecting appropriate collaboration partners may require group awareness. In a field study at two scientific events on technology-enhanced learning (Alpine Rendez-Vous 2011 and 2013) including N = 5736 relations between 287 researchers, we investigated how researchers selected future collaboration partners, looking specifically at the role of career level, disciplinary background, and selection patterns. Face-to-face contact was measured using RFID devices. Additionally, a group awareness intervention was experimentally varied. Data was analyzed using RSiena and meta-analyses. The results showed that transitivity, reciprocity and contact duration are relevant for the identification of new potential collaboration partners. PhD students were less often chosen as new potential collaboration partners, and researchers with a background in Information Technology selected fewer new potential collaboration partners. However, group awareness support balanced this disciplinary difference. Theoretical, methodological, and practical implications of these findings are discussed.

show abstract

“…A user model [3] based on users' reviews from Yelp by using support vector machine classifier is introduced. Alshareef et al [4] aim to identify the suitable venues, and researchers within a citation network by using IEEE dataset. They integrate the authors' similarities, the topical similarity, and the venues' similarities among a citation network of a given article.…”

Section: Literature Reviewmentioning

confidence: 99%

A Hybrid Approach for the Recommendation of Scholarly Journals

Abbasi

Abbas

Hammad

et al. 2020

2020 International Conference on Information Science and Communication Technology (ICISCT)

View full text Add to dashboard Cite

The increasing number of scholarly journals have made it difficult for authors to select the most suitable journal that publishes their research. Existing search systems that recommend journals for manuscript submission are either based on author's profile, bibliographic data or the copublication network. These approaches are not useful for beginner researchers who have no publication records or for those who are interested in new research domains. The present work proposes a hybrid approach that combines clustering and document similarity for the recommendation of scholarly venues. The proposal was evaluated both objectively and subjectively using domain experts. The results of mean average precision (0.84) and normalized discounted cumulative gain (0.89) shows positive recommendations made by the proposed approach.

show abstract

Recommending Scientific Collaboration Based on Topical, Authors and Venues Similarities

Cited by 14 publications

References 20 publications

Academic Venue Recommendations Based on Similarity Learning of an Extended Nearby Citation Network

Academic Venue Recommendations Based on Similarity Learning of an Extended Nearby Citation Network

Initiating scientific collaborations across career levels and disciplines – a network analysis on behavioral data

A Hybrid Approach for the Recommendation of Scholarly Journals

Contact Info

Product

Resources

About