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Scientometrics

, Volume 121, Issue 2, pp 937–956 | Cite as

Citation recommendation based on citation tendency

  • Xi Chen
  • Huan-jing Zhao
  • Shu ZhaoEmail author
  • Jie Chen
  • Yan-ping Zhang
Article
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Abstract

Due to the development of academic, more and more attentions are paid to citation recommendation. To solve the citation recommendation problem, researchers begin to focus on the network representation, because it fuses semantic information and structural information well. It is a big challenge that how to map articles in a heterogeneous information network into a low-dimensional space while preserving the potential associations between articles. We propose a novel citation recommendation algorithm based on citation tendency, named CIRec which learns more about the potential relationship of articles in the process of network embedding. Citation tendency means if an article can be selected as a reference, it probability satisfies some kinds of conditions. In our algorithm, five weight matrices which represent the probability of entity-to-entity migration based on citation tendency are defined to build weighted heterogeneous network first. Second, we design a biased random walk procedure which efficiently explores articles’ characteristics and citations information. Finally, the skip-gram model is used to learn the neighborhood relationship of the nodes in the walk sequence and map the nodes to the vector space. Comparing with existing state-of-the-art technique, experiment results show that our algorithm CIRec has better recall, precision, NDCG on AAN and DBLP dataset.

Keywords

Citation recommendation Citation tendency Heterogeneous information network Network representation 

Mathematics Subject Classification

68T99 

JEL Classification

C63 C89 
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Notes

Acknowledgements

This work was partially supported by National Natural Science Foundation of China (Grants \#61876001, \#61602003 and \#61673020), National Key Research and Development Program of China (Grant \#2017YFB1401903), the Provincial Natural Science Foundation of Anhui Province (Grant \#1708085QF156), and the Recruitment Project of Anhui University for Academic and Technology Leader.

References

  1. Ayala-Gómez, F., Daróczy, B., Benczúr, A., Mathioudakis, M., & Gionis, A. (2018). Global citation recommendation using knowledge graphs. Journal of Intelligent & Fuzzy Systems, 34(5), 3089–3100.CrossRefGoogle Scholar
  2. Bradshaw, S. (2003). Reference directed indexing: Redeeming relevance for subject search in citation indexes. In International conference on theory and practice of digital libraries (pp. 499–510): Springer.Google Scholar
  3. Cai, X., Han, J., Li, W., Zhang, R., Pan, S., & Yang, L. (2018a). A three-layered mutually reinforced model for personalized citation recommendation. IEEE Transactions on Neural Networks and Learning Systems, 29, 6026–6037.CrossRefGoogle Scholar
  4. Cai, X., Han, J., & Yang, L. (2018b) Generative adversarial network based heterogeneous bibliographic network representation for personalized citation recommendation. In AAAI, New Orleans, USA.Google Scholar
  5. Chandrasekaran, K., Gauch, S., Lakkaraju, P., & Luong, H. P. (2008). Concept-based document recommendations for citeseer authors. In International conference on adaptive hypermedia and adaptive web-based systems (pp. 83–92): Springer.Google Scholar
  6. Dong, Y., Chawla, N. V., & Swami, A. (2017) metapath2vec: Scalable representation learning for heterogeneous networks. In Proceedings of the 23rd ACM SIGKDD international conference on knowledge discovery and data mining, Halifax, NS, Canada (pp. 135–144). ACM.Google Scholar
  7. Ebesu, T., & Fang, Y. (2017). Neural citation network for context-aware citation recommendation. In Proceedings of the 40th international ACM SIGIR conference on Research and Development in Information Retrieval, Shinjuku, Tokyo, Japan (pp. 1093–1096). ACM.Google Scholar
  8. Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., et al. (2014) Generative adversarial nets. In Advances in neural information processing systems, Palais des Congrès de Montréal, Montréal, Canada (pp. 2672–2680).Google Scholar
  9. Gori, M., & Pucci, A. (2006) Research paper recommender systems: A random-walk based approach. In IEEE/WIC/ACM International Conference on Web Intelligence, 2006. WI 2006, Hong Kong, China (pp. 778–781). IEEE.Google Scholar
  10. Grover, A., & Leskovec, J. (2016). node2vec: Scalable feature learning for networks. In Proceedings of the 22nd ACM SIGKDD international conference on Knowledge discovery and data mining, San Francisco, California, USA (pp. 855–864). ACM.Google Scholar
  11. Gui, H., Liu, J., Tao, F., Jiang, M., Norick, B., & Han, J. (2016). Large-scale embedding learning in heterogeneous event data. In 2016 IEEE 16th international conference on data mining (ICDM), Barcelona, Spain (pp. 907–912). IEEE.Google Scholar
  12. Guo, L., Cai, X., Hao, F., Mu, D., Fang, C., & Yang, L. (2017). Exploiting fine-grained co-authorship for personalized citation recommendation. IEEE Access, 5, 12714–12725.CrossRefGoogle Scholar
  13. Gupta, S., & Varma, V. (2017) Scientific article recommendation by using distributed representations of text and graph. In Proceedings of the 26th international conference on World Wide Web Companion, Perth, Australia (pp. 1267–1268). International World Wide Web Conferences Steering Committee.Google Scholar
  14. Huang, W., Wu, Z., Liang, C., Mitra, P., & Giles, C. L. (2015). A neural probabilistic model for context based citation recommendation. In Twenty-ninth AAAI conference on artificial intelligence.Google Scholar
  15. Jardine, J., & Teufel, S. (2014) Topical PageRank: A model of scientific expertise for bibliographic search. In Proceedings of the 14th conference of the European Chapter of the Association for Computational Linguistics, Gothenburg, Sweden (pp. 501–510).Google Scholar
  16. Jie, T., Jing, Z., Yao, L., Li, J., & Zhong, S. (2008). ArnetMiner: Extraction and mining of academic social networks. In Acm Sigkdd international conference on knowledge discovery & data mining.Google Scholar
  17. Le, Q., & Mikolov, T. (2014) Distributed representations of sentences and documents. In International conference on machine learning, Beijing, China (pp. 1188–1196).Google Scholar
  18. Fu, T.-y., Lee, W.-C., & Lei, Z. (2017). Hin2vec: Explore meta-paths in heterogeneous information networks for representation learning. In Proceedings of the 2017 ACM on conference on information and knowledge management, Singapore, Singapore (pp. 1797–1806). ACM.Google Scholar
  19. Meng, F., Gao, D., Li, W., Sun, X., & Hou, Y. (2013). A unified graph model for personalized query-oriented reference paper recommendation. In Proceedings of the 22nd ACM international conference on Information & Knowledge Management, San Francisco, California, USA (pp. 1509–1512). ACM.Google Scholar
  20. Mikolov, T., Chen, K., Corrado, G., & Dean, J. J. C. S. (2013a). Efficient estimation of word representations in vector space.Google Scholar
  21. Mikolov, T., Sutskever, I., Chen, K., Corrado, G. S., & Dean, J. (2013b) Distributed representations of words and phrases and their compositionality. In Advances in neural information processing systems, Lake Tahoe, Nevada, United States (pp. 3111–3119).Google Scholar
  22. Mu, D., Guo, L., Cai, X., & Hao, F. (2018). Query-focused personalized citation recommendation with mutually reinforced ranking. IEEE Access, 6, 3107–3119.CrossRefGoogle Scholar
  23. Nallapati, R. M., Ahmed, A., Xing, E. P., & Cohen, W. W. (2008). Joint latent topic models for text and citations. In Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 542–550). ACM.Google Scholar
  24. Pan, L., Dai, X., Huang, S., & Chen, J. (2015). Academic paper recommendation based on heterogeneous graph. Chinese computational linguistics and natural language processing based on naturally annotated big data (pp. 381–392). New York: Springer.CrossRefGoogle Scholar
  25. Perozzi, B., Al-Rfou, R., & Skiena, S. (2014). Deepwalk: Online learning of social representations. In Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining, New York, New York, USA (pp. 701–710). ACM.Google Scholar
  26. Qiu, J., Dong, Y., Ma, H., Li, J., Wang, K., & Tang, J. (2018). Network embedding as matrix factorization: Unifying deepwalk, line, pte, and node2vec. In Proceedings of the eleventh ACM international conference on Web Search and Data Mining, Marina Del Rey, CA, USA (pp. 459–467). ACM.Google Scholar
  27. Radev, D. R., Muthukrishnan, P., Qazvinian, V., & Abu-Jbara, A. (2013). The ACL anthology network corpus. Language Resources and Evaluation, 47(4), 919–944.CrossRefGoogle Scholar
  28. Seyler, D., Chandar, P., & Davis, M. (2018). An information retrieval framework for contextual suggestion based on heterogeneous information network embeddings. In The 41st international ACM SIGIR Conference on Research & Development in Information Retrieval (pp. 953–956). ACM.Google Scholar
  29. Tang, J., Qu, M., & Mei, Q. (2015a) Pte: Predictive text embedding through large-scale heterogeneous text networks. In Proceedings of the 21th ACM SIGKDD international conference on Knowledge Discovery and Data Mining, Sydney, NSW, Australia (pp. 1165–1174). ACM.Google Scholar
  30. Tang, J., Qu, M., Wang, M., Zhang, M., Yan, J., & Mei, Q. (2015b) Line: Large-scale information network embedding. In Proceedings of the 24th international conference on World Wide Web, Florence, Italy (pp. 1067–1077). International World Wide Web Conferences Steering Committee.Google Scholar
  31. Yang, L., Zheng, Y., Cai, X., Dai, H., Mu, D., Guo, L., et al. (2018). A LSTM based model for personalized context-aware citation recommendation. IEEE Access, 6, 59618–59627.CrossRefGoogle Scholar
  32. Zhang, Y., Yang, L., Cai, X., & Dai, H. (2018). A novel personalized citation recommendation approach based on GAN. In International symposium on methodologies for intelligent systems (pp. 268–278). Springer.Google Scholar
  33. Zhao, J., Mathieu, M., & LeCun, Y. J. (2016). Energy-based generative adversarial network.Google Scholar

Copyright information

© Akadémiai Kiadó, Budapest, Hungary 2019

Authors and Affiliations

  • Xi Chen
    • 1
    • 2
  • Huan-jing Zhao
    • 1
    • 2
  • Shu Zhao
    • 1
    • 2
    Email author
  • Jie Chen
    • 1
    • 2
  • Yan-ping Zhang
    • 1
    • 2
  1. 1.Key Laboratory of Intelligent Computing and Signal Processing, Ministry of EducationAhu UniversityHefeiChina
  2. 2.School of Computer Science and TechnologyAnhui UniversityHefeiChina

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