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 We learn user interests by one’s historical references or a set of query keywords.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
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
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
Bedi, P., Kaur, H., Marwaha, S.: Trust based recommender system for semantic web. IJCAI 7, 2677–2682 (2007)
Belkin, M., Niyogi, P.: Laplacian eigenmaps and spectral techniques for embedding and clustering. NIPS 14, 585–591 (2001)
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)
Chen, T., Sun, Y.: Task-guided and path-augmented heterogeneous network embedding for author identification (2016)
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)
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)
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)
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)
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
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)
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)
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)
Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8) (2009)
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)
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)
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)
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)
Moreira, C., Calado, P., Martins, B.: Learning to rank academic experts in the DBLP dataset. Expert. Syst. 32(4), 477–493 (2015)
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)
Priem, J., Piwowar, H., Hemminger, B.: Altmetrics in the wild: Using social media to explore scholarly impact (2012)
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)
Sugiyama, K., Kan, M.: A comprehensive evaluation of scholarly paper recommendation using potential citation papers. Int. J. Digit. Libr. 16(2), 91–109 (2015)
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)
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)
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)
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)
Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000)
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)
Yates, E., Dixon, L.: Pagerank as a method to rank biomedical literature by importance. Source Code Biol. Med. 10(1), 16 (2015)
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)
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)
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
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Singapore Pte Ltd.
About this paper
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)
