More Web Proxy on the site http://driver.im/

research-article

ACRec: a co-authorship based random walk model for academic collaboration recommendation

Authors:

Nana Yaw Asabere,

Huizhen JiangAuthors Info & Claims

WWW '14 Companion: Proceedings of the 23rd International Conference on World Wide Web

Pages 1209 - 1214

https://doi.org/10.1145/2567948.2579034

Published: 07 April 2014 Publication History

Abstract

Recent academic procedures have depicted that work involving scientific research tends to be more prolific through collaboration and cooperation among researchers and research groups. On the other hand, discovering new collaborators who are smart enough to conduct joint-research work is accompanied with both difficulties and opportunities. One notable difficulty as well as opportunity is the big scholarly data. In this paper, we satisfy the demand of collaboration recommendation through co-authorship in an academic network. We propose a random walk model using three academic metrics as basics for recommending new collaborations. Each metric is studied through mutual paper co-authoring information and serves to compute the link importance such that a random walker is more likely to visit the valuable nodes. Our experiments on DBLP dataset show that our approach can improve the precision, recall rate and coverage rate of recommendation, compared with other state-of-the-art approaches.

References

[1]

L. Backstrom and J. Leskovec. Supervised random walks: Predicting and recommending links in social networks. In Proc. of ACM WSDM'11, pages 635--644, Hong Kong, China, 2011.

Digital Library

[2]

D. M. Boyd and N. B. Ellison. Social network sites: Definition, history, and scholarship. Journal of Computer-Mediated Communication, 13(1):210--230, 2007.

Digital Library

[3]

M. A. Brandao, M. M. Moro, G. R. Lopes, and J. P. Oliveira. Using link semantics to recommend collaborations in academic social networks. In Proc. of WWW'13 Companion, pages 833--840, 2013.

Digital Library

[4]

H.-H. Chen, L. Gou, X. Zhang, and C. L. Giles. Collabseer: A search engine for collaboration discovery. In Proc. of ACM/IEEE JCDL'11, pages 231--240, Ottawa, Ontario, Canada, 2011.

Digital Library

[5]

F. Fouss, A. Pirotte, J.-M. Renders, and M. Saerens. Random-walk computation of similarities between nodes of a graph with application to collaborative recommendation. IEEE Transactions on Knowledge and Data Engineering, 19(3):355--369, 2007.

Digital Library

[6]

J. Freyne, S. Berkovsky, E. M. Daly, and W. Geyer. Social networking feeds: Recommending items of interest. In Proc. of ACM RecSys'10, pages 277--280.

Digital Library

[7]

M. Jamali and M. Ester. Trustwalker: A random walk model for combining trust-based and item-based recommendation. In Proc. of ACM KDD'09, pages 397--406, Paris, France, 2009.

Digital Library

[8]

S. Lee and B. Bozeman. The impact of research collaboration on scientific productivity. Social Studies of Science, 35(5):673--702, 2005.

[9]

M. Ley. Dblp: some lessons learned. Proceedings of the VLDB Endowment, 2(2):1493--1500, 2009.

Digital Library

[10]

G. Lopes, M. M. Moro, L. K. Wives, and J. P. M. de Oliveira. Collaboration recommendation on academic social networks. In Advances in Conceptual Modeling IC Applications and Challenges, volume 6413 of Lecture Notes in Computer Science, pages 190--199. Springer Berlin Heidelberg, 2010.

Digital Library

[11]

H. Ma, D. Zhou, C. Liu, M. R. Lyu, and I. King. Recommender systems with social regularization. In Proc. of ACM WSDM'11, pages 287--296, Hong Kong, China, 2011.

Digital Library

[12]

M. E. J. Newman. Scientific collaboration networks. i. network construction and fundamental results. Physical Review E, 64(1):016131, Jun 2001.

[13]

S. Perugini, M. A. Goncalves, and E. A. Fox. Recommender systems research: A connection-centric survey. Journal of Intelligent Information Systems, 23(2):107--143, 2004.

Digital Library

[14]

J. Tang, S. Wu, J. Sun, and H. Su. Cross-domain collaboration recommendation. In Proc. of ACM KDD'12, pages 1285--1293, Beijing, China, 2012. ACM.

Digital Library

[15]

J. Tang, J. Zhang, L. Yao, J. Li, L. Zhang, and Z. Su. Arnetminer: extraction and mining of academic social networks. In Proc. of ACM KDD'08, pages 990--998, Las Vegas, Nevada, USA, 2008. ACM.

Digital Library

[16]

H. Tong, C. Faloutsos, and J.-Y. Pan. Random walk with restart: Fast solutions and applications. Knowledge and Information Systems, 14(3):327--346, 2008.

Digital Library

[17]

A. Toscher, M. Jahrer, and R. Legenstein. Improved neighborhood-based algorithms for large-scale recommender systems. In Proc. of NETFLIX'08, pages 4:1--4:6, Las Vegas, Nevada, 2008. ACM.

Digital Library

Cited By

Verma SHarit SMunjal K(2024)SRRS: Design and Development of a Scholarly Reciprocal Recommendation SystemScientometrics10.1007/s11192-024-05143-8129:11(6839-6866)Online publication date: 21-Sep-2024
https://doi.org/10.1007/s11192-024-05143-8
Peng XShi J(2024)The drivers, features, and influence of first scientific collaboration among core scholars from Chinese library and information fieldJournal of the Association for Information Science and Technology10.1002/asi.24888Online publication date: 20-Mar-2024
https://doi.org/10.1002/asi.24888
Zhao MHuang XZhu LSang JYu J(2023)Knowledge Graph-Enhanced Sampling for Conversational Recommendation SystemIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2022.318515435:10(9890-9903)Online publication date: 1-Oct-2023
https://doi.org/10.1109/TKDE.2022.3185154
Show More Cited By

Index Terms

ACRec: a co-authorship based random walk model for academic collaboration recommendation
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Document filtering
      2. Information extraction

Recommendations

AVER: Random Walk Based Academic Venue Recommendation
WWW '15 Companion: Proceedings of the 24th International Conference on World Wide Web

Academic venues act as the main platform of communities in academia and the bridge of connecting researchers, which have rapidly developed in recent years. However, information overload in big scholarly data creates tremendous challenges for mining ...
On the Correlation Between Research Complexity and Academic Competitiveness
Digital Libraries at Times of Massive Societal Transition
Abstract
Academic capacity is a common way to reflect the educational level of a country or district. The aim of this study is to explore the difference between the scientific research level of institutions and countries. By proposing an indicator named ...
Dynamic network analytics for recommending scientific collaborators
Abstract
Collaboration is one of the most important contributors to scientific advancement and a crucial aspect of an academic’s career. However, the explosion in academic publications has, for some time, been making it more challenging to find suitable ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Other conferences

WWW '14 Companion: Proceedings of the 23rd International Conference on World Wide Web

April 2014

1396 pages

ISBN:9781450327459

DOI:10.1145/2567948

General Chair:
Chin-Wan Chung
Korea Advanced Institute of Science and Technology, Korea
,
Program Chairs:
Andrei Broder
Google Inc., USA
,
Kyuseok Shim
Seoul National University, Korea
,
Torsten Suel
New York University, USA

Copyright © 2014 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

IW3C2: International World Wide Web Conference Committee

In-Cooperation

SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 07 April 2014

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Conference

WWW '14

Sponsor:

IW3C2

WWW '14: 23rd International World Wide Web Conference

April 7 - 11, 2014

Seoul, Korea

Acceptance Rates

Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

48
Total Citations
View Citations
543
Total Downloads

Downloads (Last 12 months)24
Downloads (Last 6 weeks)9

Reflects downloads up to 11 Dec 2024

Other Metrics

View Author Metrics

Citations

Cited By

Verma SHarit SMunjal K(2024)SRRS: Design and Development of a Scholarly Reciprocal Recommendation SystemScientometrics10.1007/s11192-024-05143-8129:11(6839-6866)Online publication date: 21-Sep-2024
https://doi.org/10.1007/s11192-024-05143-8
Peng XShi J(2024)The drivers, features, and influence of first scientific collaboration among core scholars from Chinese library and information fieldJournal of the Association for Information Science and Technology10.1002/asi.24888Online publication date: 20-Mar-2024
https://doi.org/10.1002/asi.24888
Zhao MHuang XZhu LSang JYu J(2023)Knowledge Graph-Enhanced Sampling for Conversational Recommendation SystemIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2022.318515435:10(9890-9903)Online publication date: 1-Oct-2023
https://doi.org/10.1109/TKDE.2022.3185154
Liu BWang B(2023)Bayesian Negative Sampling for Recommendation2023 IEEE 39th International Conference on Data Engineering (ICDE)10.1109/ICDE55515.2023.00063(749-761)Online publication date: Apr-2023
https://doi.org/10.1109/ICDE55515.2023.00063
Gayen AMukherjea TJana A(2023)Characterization of ‘Early Career Stage’ Researchers from their Co-authorship Network2023 IEEE Guwahati Subsection Conference (GCON)10.1109/GCON58516.2023.10183410(1-6)Online publication date: 23-Jun-2023
https://doi.org/10.1109/GCON58516.2023.10183410
Yang CWang CZheng RGeng S(2023)Link prediction in research collaboration: a multi-network representation learning framework with joint trainingMultimedia Tools and Applications10.1007/s11042-023-15720-382:30(47215-47233)Online publication date: 8-May-2023
https://doi.org/10.1007/s11042-023-15720-3
Zhang ZPatra BYaseen AZhu JSabharwal RRoberts KCao TWu H(2023)Scholarly recommendation systems: a literature surveyKnowledge and Information Systems10.1007/s10115-023-01901-x65:11(4433-4478)Online publication date: 4-Jun-2023
https://doi.org/10.1007/s10115-023-01901-x
Ma XDeng QYe YYang TZeng J(2022)Attention based Collaborator Recommendation in Heterogeneous Academic Networks2022 IEEE 25th International Conference on Computational Science and Engineering (CSE)10.1109/CSE57773.2022.00017(51-58)Online publication date: Dec-2022
https://doi.org/10.1109/CSE57773.2022.00017
Dong CJu XMa Y(2021)HRS: Hybrid Recommendation System based on Attention Mechanism and Knowledge Graph EmbeddingIEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology10.1145/3498851.3498987(406-413)Online publication date: 14-Dec-2021
https://dl.acm.org/doi/10.1145/3498851.3498987
Zhou XLiang WWang KHuang RJin Q(2021)Academic Influence Aware and Multidimensional Network Analysis for Research Collaboration Navigation Based on Scholarly Big DataIEEE Transactions on Emerging Topics in Computing10.1109/TETC.2018.28600519:1(246-257)Online publication date: 1-Jan-2021
https://doi.org/10.1109/TETC.2018.2860051
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents