Abstract
The overabundance of academic papers makes it difficult for researchers to find relevant or interested papers. To address the problem, existing studies have developed many approaches to recommend academic papers effectively. Most of them mainly utilize content-based filtering or citation analysis to measure similarity or relatedness of two papers and recommend relevant papers to the given query. However, these recommended papers are usually discrete from each other, i.e., the relationship between recommended papers are omitted, which disables researchers from having an sight into the time-oriented development of the topic they are interested in. To overcome the drawbacks of existing work, we propose a novel academic paper recommendation method called PAPR (Path-based Academic Paper Recommendation). Our method aims to recommend an ordered path of relevant papers, which are of great benefit in helping researchers understand the development of a specific topic. During process, we take both content and network structure into account to learn the representation of a paper. Next, the similarity between papers are measured based on the representation. The experimental results based on real data show that the proposed method outperforms the state-of-art methods.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent Dirichlet allocation. J. Mach. Learn. Res. 3, 993–1022 (2003)
Chen, T.T., Lee, M.: Research paper recommender systems on big scholarly data. In: Yoshida, K., Lee, M. (eds.) PKAW 2018. LNCS (LNAI), vol. 11016, pp. 251–260. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-97289-3_20
Deerwester, S., Dumais, S.T., Furnas, G.W., Landauer, T.K., Harshman, R.: Indexing by latent semantic analysis. J. Am. Soc. Inform. Sci. 41(6), 391–407 (1990)
Dong, Y., Chawla, N.V., Swami, A.: metapath2vec: scalable representation learning for heterogeneous networks. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 135–144 (2017)
Grover, A., Leskovec, J.: node2vec: scalable feature learning for networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 855–864 (2016)
Hammou, B.A., Lahcen, A.A., Mouline, S.: APRA: an approximate parallel recommendation algorithm for big data. Knowl.-Based Syst. 157, 10–19 (2018)
Lao, N., Cohen, W.W.: Relational retrieval using a combination of path-constrained random walks. Mach. Learn. 81(1), 53–67 (2010)
Liu, H., Kong, X., Bai, X., Wang, W., Bekele, T.M., Xia, F.: Context-based collaborative filtering for citation recommendation. IEEE Access 3, 1695–1703 (2015)
Nascimento, C., Laender, A.H., da Silva, A.S., Gonçalves, M.A.: A source independent framework for research paper recommendation. In: Proceedings of the 11th Annual International ACM/IEEE Joint Conference on Digital Libraries, pp. 297–306 (2011)
Page, L., Brin, S., Motwani, R., Winograd, T.: The PageRank citation ranking: bringing order to the web. Proceedings of the WWW Conference, vol. 1998, pp. 161–172 (1999)
Pazzani, M.J., Billsus, D.: Content-based recommendation systems. In: Brusilovsky, P., Kobsa, A., Nejdl, W. (eds.) The Adaptive Web. LNCS, vol. 4321, pp. 325–341. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-72079-9_10
Perozzi, B., Al-Rfou, R., Skiena, S.: DeepWalk: online learning of social representations. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 701–710 (2014)
Schafer, J.B., Frankowski, D., Herlocker, J., Sen, S.: Collaborative filtering recommender systems. In: Brusilovsky, P., Kobsa, A., Nejdl, W. (eds.) The Adaptive Web. LNCS, vol. 4321, pp. 291–324. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-72079-9_9
Shan, H., Banerjee, A.: Generalized probabilistic matrix factorizations for collaborative filtering. In: 2010 IEEE International Conference on Data Mining, pp. 1025–1030. IEEE (2010)
Shi, C., Kong, X., Huang, Y., Philip, S.Y., Wu, B.: HeteSim: a general framework for relevance measure in heterogeneous networks. IEEE Trans. Knowl. Data Eng. 26(10), 2479–2492 (2014)
Small, H.: Co-citation in the scientific literature: a new measure of the relationship between two documents. J. Am. Soc. Inform. Sci. 24(4), 265–269 (1973)
Sun, Y., Han, J., Yan, X., Yu, P.S., Wu, T.: PathSim: meta path-based top-K similarity search in heterogeneous information networks, vol. 4, pp. 992–1003. CiteSeer (2011)
Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. In: Advances in Neural Information Processing Systems, pp. 3104–3112 (2014)
Tang, J., Zhang, J., Yao, L., Li, J., Zhang, L., Su, Z.: ArnetMiner: extraction and mining of academic social networks. In: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 990–998 (2008)
Tian, G., Jing, L.: Recommending scientific articles using bi-relational graph-based iterative RWR. In: Proceedings of the 7th ACM Conference on Recommender Systems, pp. 399–402 (2013)
Wang, C., Blei, D.M.: Collaborative topic modeling for recommending scientific articles. In: Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 448–456 (2011)
Zhou, D., et al.: Learning multiple graphs for document recommendations. In: Proceedings of the 17th International Conference on World Wide Web, pp. 141–150 (2008)
Acknowledgment
This work was supported by the Major Program of the Natural Science Foundation of Jiangsu Higher Education Institutions of China under Grant No. 19KJA610002 and 19KJB520050, and the National Natural Science Foundation of China under Grant No. 61902270, a project funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Hua, S., Chen, W., Li, Z., Zhao, P., Zhao, L. (2020). Path-Based Academic Paper Recommendation. In: Huang, Z., Beek, W., Wang, H., Zhou, R., Zhang, Y. (eds) Web Information Systems Engineering – WISE 2020. WISE 2020. Lecture Notes in Computer Science(), vol 12343. Springer, Cham. https://doi.org/10.1007/978-3-030-62008-0_24
Download citation
DOI: https://doi.org/10.1007/978-3-030-62008-0_24
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-62007-3
Online ISBN: 978-3-030-62008-0
eBook Packages: Computer ScienceComputer Science (R0)