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Cited 32 time in webofscience Cited 36 time in scopus
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Global citation recommendation employing generative adversarial network

Authors
Ali, Z[Ali, Zafar]Qi, GL[Qi, Guilin]Muhammad, K[Muhammad, Khan]Kefalas, P[Kefalas, Pavlos]Khusro, S[Khusro, Shah]
Issue Date
15-Oct-2021
Publisher
PERGAMON-ELSEVIER SCIENCE LTD
Keywords
Recommender systems; Citation recommendation; Network embedding; Generative adversarial network; Deep learning; Sparsity
Citation
EXPERT SYSTEMS WITH APPLICATIONS, v.180
Indexed
SCIE
SCOPUS
Journal Title
EXPERT SYSTEMS WITH APPLICATIONS
Volume
180
URI
https://scholarx.skku.edu/handle/2021.sw.skku/96004
DOI
10.1016/j.eswa.2021.114888
ISSN
0957-4174
Abstract
The variety and plethora of research papers available on the Web motivated researchers to propose models that could assist users with personalized citation recommendations. In recent years, citation recommendation models using Network Representation Learning (NRL) methods have shown promising results. Nevertheless, existing NRL-based models are limited in terms of exploiting semantic relations and contextual information between the objects of bibliographic papers' networks. Additionally, these models cannot adequately explore the structure of heterogeneous information networks, topical relevance, and relevant semantics. Consequently, they suffer from network sparsity and inadequate personalization problems. To overcome these shortcomings, we present a network embedding model termed as Global Citation Recommendation employing Generative Adversarial Network (GCR-GAN). The proposed model exploits the Heterogeneous Bibliographic Network (HBN) to generate personalized citation recommendations. In particular, the proposed model utilizes semantic relations corresponding to the objects of the heterogeneous bibliographic network and captures network structure proximity employing the Scientific Paper Embeddings using Citation-informed Transformers (SPECTER) and Denoising Auto-encoder networks to learn semantic-preserving graph representations. Compared to baseline models, the recommendations generated by our model over the DBLP and ACM datasets prove that it outperforms baseline methods by gaining almost 11% and 12% improvement in terms of Mean Average Precision (MAP) and Normalized Discounted Cumulative Gain (nDCG) metrics, respectively. Furthermore, we analyzed the effectiveness of the proposed model considering network sparsity issue, where our model gains almost 7% better recall@100 score against the second-best counterpart.
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