Skip to main content
SpringerLink
Log in

Embedding Based Personalized New Paper Recommendation

  • Conference paper
  • First Online:
Computer Supported Cooperative Work and Social Computing (ChineseCSCW 2020)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1330))

Abstract

It is meaningful for researchers to find the interested and high quality new papers. We propose the Joint Text and Influence Embedding recommendation model (JTIE) to consider both the paper quality and the content correlation. We train a paper embedding based on its core elements: contents, authors and publication venues. The quality of a new paper is evaluated based on the author authority and the venue reputation. The citation relationships between papers are considered asymmetric such that they can reflect the user’s consideration on the intrinsic influence of a paper. We learn user interests by one’s historical references or a set of query keywords. Finally, papers are recommended according to the relatedness between user interests and paper embeddings. We perform experiments against three real-world datasets. The results show that our model outperforms baseline methods on both the personalized recommendation and the query keywords based retrieval.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 119.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 159.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

Notes

  1. 1.

    https://www.scopus.com/.

  2. 2.

    https://bulkdata.USPTO.gov.

References

  1. Asiwal, K., Suresh, B.K., Reddy, G.R.M.: Analysis of academic research networks to find collaboration partners. In: Stephanidis, C. (ed.) HCI 2016. CCIS, vol. 618, pp. 8–14. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-40542-1_2

    Chapter  Google Scholar 

  2. Bedi, P., Kaur, H., Marwaha, S.: Trust based recommender system for semantic web. IJCAI 7, 2677–2682 (2007)

    Google Scholar 

  3. Belkin, M., Niyogi, P.: Laplacian eigenmaps and spectral techniques for embedding and clustering. NIPS 14, 585–591 (2001)

    Google Scholar 

  4. Chakraborty, T., Modani, N., Narayanam, R., Nagar, S.: Discern: a diversified citation recommendation system for scientific queries. In: 2015 IEEE 31st International Conference on Data Engineering (ICDE), pp. 555–566. IEEE (2015)

    Google Scholar 

  5. Chen, T., Sun, Y.: Task-guided and path-augmented heterogeneous network embedding for author identification (2016)

    Google Scholar 

  6. Chikhaoui, B., Chiazzaro, M., Wang, S.: A new granger causal model for influence evolution in dynamic social networks: the case of DBLP. In: AAAI, pp. 51–57 (2015)

    Google Scholar 

  7. Gu, Q., Zhou, J., Ding, C.: Collaborative filtering: weighted nonnegative matrix factorization incorporating user and item graphs. In: Proceedings of the 2010 SIAM International Conference on Data Mining, pp. 199–210. SIAM (2010)

    Google Scholar 

  8. Gutmann, M.U., Hyvärinen, A.: Noise-contrastive estimation of unnormalized statistical models, with applications to natural image statistics. J. Mach. Learn. Res. 13(Feb), 307–361 (2012)

    Google Scholar 

  9. Guy, I., et al.: Personalized recommendation of social software items based on social relations. In: Proceedings of the Third ACM Conference on Recommender Systems, pp. 53–60. ACM (2009)

    Google Scholar 

  10. He, J., Chu, W.W.: A social network-based recommender system (SNRS). In: Memon, N., Xu, J., Hicks, D., Chen, H. (eds.) Data Mining for Social Network Data. Annals of Information Systems, vol. 12. Springer, Boston (2010). https://doi.org/10.1007/978-1-4419-6287-4_4

  11. He, X., Liao, L., Zhang, H., Nie, L., Hu, X., Chua, T.S.: Neural collaborative filtering. In: Proceedings of the 26th International Conference on World Wide Web, pp. 173–182. International World Wide Web Conferences Steering Committee (2017)

    Google Scholar 

  12. Hu, Y., Koren, Y., Volinsky, C.: Collaborative filtering for implicit feedback datasets. In: Eighth IEEE International Conference on Data Mining. ICDM 2008, pp. 263–272. IEEE (2008)

    Google Scholar 

  13. Järvelin, K., Kekäläinen, J.: IR evaluation methods for retrieving highly relevant documents. In: Proceedings of the 23rd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 41–48. ACM (2000)

    Google Scholar 

  14. Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8) (2009)

    Google Scholar 

  15. Liu, J., Shang, J., Wang, C., Ren, X., Han, J.: Mining quality phrases from massive text corpora. In: Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data, pp. 1729–1744. ACM (2015)

    Google Scholar 

  16. Ma, H., Yang, H., Lyu, M.R., King, I.: Sorec: social recommendation using probabilistic matrix factorization. In: Proceedings of the 17th ACM Conference on Information and Knowledge Management, pp. 931–940. ACM (2008)

    Google Scholar 

  17. McNee, S.M., et al.: On the recommending of citations for research papers. In: Proceedings of the 2002 ACM Conference on Computer Supported Cooperative Work, pp. 116–125. ACM (2002)

    Google Scholar 

  18. Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. In: Advances in Neural Information Processing Systems, pp. 3111–3119 (2013)

    Google Scholar 

  19. Moreira, C., Calado, P., Martins, B.: Learning to rank academic experts in the DBLP dataset. Expert. Syst. 32(4), 477–493 (2015)

    Article  Google Scholar 

  20. 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. ACM (2014)

    Google Scholar 

  21. Priem, J., Piwowar, H., Hemminger, B.: Altmetrics in the wild: Using social media to explore scholarly impact (2012)

    Google Scholar 

  22. Song, M., Heo, G., Kim, S.: Analyzing topic evolution in bioinformatics: investigation of dynamics of the field with conference data in DBLP. Scientometrics 101(1), 397–428 (2014)

    Article  Google Scholar 

  23. Sugiyama, K., Kan, M.: A comprehensive evaluation of scholarly paper recommendation using potential citation papers. Int. J. Digit. Libr. 16(2), 91–109 (2015)

    Article  Google Scholar 

  24. Sugiyama, K., Kan, M.Y.: Exploiting potential citation papers in scholarly paper recommendation. In: Proceedings of the 13th ACM/IEEE-CS Joint Conference on Digital Libraries, pp. 153–162. ACM (2013)

    Google Scholar 

  25. Tang, J., Qu, M., Mei, Q.: 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, pp. 1165–1174. ACM (2015)

    Google Scholar 

  26. Tang, J., Qu, M., Wang, M., Zhang, M., Yan, J., Mei, Q.: Line: large-scale information network embedding. In: Proceedings of the 24th International Conference on World Wide Web, pp. 1067–1077. ACM (2015)

    Google Scholar 

  27. 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. ACM (2008)

    Google Scholar 

  28. Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000)

    Google Scholar 

  29. Xie, Y., Sun, Y., Shen, L.: Predicating paper influence in academic network. In: 2016 IEEE 20th International Conference on Computer Supported Cooperative Work in Design (CSCWD), pp. 539–544. IEEE (2016)

    Google Scholar 

  30. Yates, E., Dixon, L.: Pagerank as a method to rank biomedical literature by importance. Source Code Biol. Med. 10(1), 16 (2015)

    Article  Google Scholar 

  31. Zhang, A., Bhardwaj, A., Karger, D.: Confer: a conference recommendation and meetup tool. In: Proceedings of the 19th ACM Conference on Computer Supported Cooperative Work and Social Computing Companion, pp. 118–121. ACM (2016)

    Google Scholar 

  32. Zhang, S., Wang, W., Ford, J., Makedon, F.: Learning from incomplete ratings using non-negative matrix factorization. In: Proceedings of the 2006 SIAM International Conference on Data Mining, pp. 549–553. SIAM (2006)

    Google Scholar 

Download references

Acknowledgement

This work was supported by the National Key R&D Program of China (2018YFC0831401), the Key R&D Program of Shandong Province (2019JZZY010107), the National Natural Science Foundation of China (91646119), the Major Project of NSF Shandong Province (ZR2018ZB0420), and the Key R&D Program of Shandong province (2017GGX10114). The scientific calculations in this paper have been done on the HPC Cloud Platform of Shandong University.

Author information

Authors and Affiliations

  1. School of Software, Shandong University, Jinan, China

    Yi Xie, Wei Pan & Yuqing Sun

  2. School of Computer Science and Technology, Shandong University, Jinan, China

    Yi Xie & Shaoqing Wang

  3. School of Microelectronics, Shandong University, Jinan, China

    Huaibin Tang

  4. Engineering Research Center of Digital Media Technology, Shandong University, Jinan, China

    Yuqing Sun

Authors
  1. Yi Xie
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Shaoqing Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Wei Pan
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Huaibin Tang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Yuqing Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yuqing Sun .

Editor information

Editors and Affiliations

  1. Shandong University, Jinan, China

    Yuqing Sun

  2. Guangdong University of Technology, Guangzhou, China

    Dongning Liu

  3. Shenzhen University, Shenzhen, China

    Hao Liao

  4. Tongji University, Shanghai, China

    Hongfei Fan

  5. University of Shanghai for Science and Technology, Shanghai, China

    Liping Gao

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Xie, Y., Wang, S., Pan, W., Tang, H., Sun, Y. (2021). Embedding Based Personalized New Paper Recommendation. In: Sun, Y., Liu, D., Liao, H., Fan, H., Gao, L. (eds) Computer Supported Cooperative Work and Social Computing. ChineseCSCW 2020. Communications in Computer and Information Science, vol 1330. Springer, Singapore. https://doi.org/10.1007/978-981-16-2540-4_40

Download citation

  • DOI: https://doi.org/10.1007/978-981-16-2540-4_40

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-16-2539-8

  • Online ISBN: 978-981-16-2540-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Societies and partnerships

Search

Navigation