- Title
- Venue topic model-enhanced joint graph modelling for citation recommendation in scholarly big data
- Creator
- Wang, Wei; Gong, Zhiguo; Ren, Jing; Xia, Feng; Lv, Zhihan; Wei, Wei
- Date
- 2021
- Type
- Text; Journal article
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/176624
- Identifier
- vital:15170
- Identifier
-
https://doi.org/10.1145/3404995
- Identifier
- ISBN:2375-4699 (ISSN)
- Abstract
- Natural language processing technologies, such as topic models, have been proven to be effective for scholarly recommendation tasks with the ability to deal with content information. Recently, venue recommendation is becoming an increasingly important research task due to the unprecedented number of publication venues. However, traditional methods focus on either the author's local network or author-venue similarity, where the multiple relationships between scholars and venues are overlooked, especially the venue-venue interaction. To solve this problem, we propose an author topic model-enhanced joint graph modeling approach that consists of venue topic modeling, venue-specific topic influence modeling, and scholar preference modeling. We first model the venue topic with Latent Dirichlet Allocation. Then, we model the venue-specific topic influence in an asymmetric and low-dimensional way by considering the topic similarity between venues, the top-influence of venues, and the top-susceptibility of venues. The top-influence characterizes venues' capacity of exerting topic influence on other venues. The top-susceptibility captures venues' propensity of being topically influenced by other venues. Extensive experiments on two real-world datasets show that our proposed joint graph modeling approach outperforms the state-of-The-Art methods. © 2020 ACM.
- Publisher
- Association for Computing Machinery
- Relation
- ACM Transactions on Asian and Low-Resource Language Information Processing Vol. 20, no. 1 (2021), p.
- Rights
- All metadata describing materials held in, or linked to, the repository is freely available under a CC0 licence
- Rights
- Copyright © 2020 ACM.
- Rights
- Open Access
- Subject
- 08 Information and Computing Sciences; academic information retrieval; natural language processing; Network embedding; scientific collaboration
- Full Text
- Reviewed
- Funder
- This work is funded by National Key R&D Program of China (Grant No: 2019YFB1600700), The Science and Technology Development Fund, Macau SAR (SKL-IOTSC-2018-2020, FDCT/0045/2019/A1, FDCT/007/2016/AFJ), Guangzhou Science and Technology Innovation and Development Commission (EF005/FST-GZG/2019/GSTIC), Research Committee of University of Macau (MYRG2017-00212-FST, MYRG2018-00129-FST), National Natural Science Foundation of China (No. 61902203) and Key Research and Development Plan-Major Scientific, Technological Innovation Projects of ShanDong Province (2019JZZY020101), and China Postdoctoral Science Foundation (2019M651115). Authors’ addresses: W. Wang and Z. Gong, State Key Laboratory of Internet of Things for Smart City and Department of Computer and Information Science, University of Macau, Taipa, Macau, 999078, China; emails: wwang@um.edu.mo, fstzgg@um.edu.mo; J. Ren, School of Software, Dalian University of Technology, Dalian 116620, China; email: ch.yum@outlook.com; F. Xia (corresponding author), School of Engineering, IT and Physical Sciences, Federation University Australia, Ballarat, VIC 3353, Australia; email: f.xia@acm.org; Z. Lv, School of Data Science and Software Engineering, Qingdao University, Qingdao 266071, China; email: lvzhihan@gmail.com; W. Wei, School of Computer Science and Engineering, Xi’an University of Technology, Xi’an 710054, China; email: weiwei@xaut.edu.cn. 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 permissions@acm.org. © 2021 Association for Computing Machinery. 2375-4699/2021/02-ART4 $15.00 https://doi.org/10.1145/3404995
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