Abstract
Managing individual expertise is a major concern within any industrial-wide organization. If previous works have extensively studied the related expertise and authority profiling issues, they assume a semantic independence of these two key concepts. In digital libraries, state-of-the-art models generally summarize the researchers’ profile by using solely textual information. Consequently, authors with a large amount of publications are mechanically fostered to the detriment of less prolific ones with probably higher expertise. To overcome this drawback we propose to merge the two representations of expertise and authority and balance the results by capturing a mutual reinforcement principle between these two notions. Based on a graph representation of the library, the expert profiling task is formulated as an optimization problem where latent expertise and authority representations are learned simultaneously, unbiasing the expertise scores of individuals with a large amount of publications. The proposal is instanciated on a public scientific bibliographic dataset where researchers’ publications are considered as a source of evidence of individuals’ expertise and citation relations as a source of authoritative signals. Results from our experiments conducted over the Microsoft Academic Search database demonstrate significant efficiency improvement in comparison with state-of-the-art models for the expert retrieval task.
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
Notes
- 1.
- 2.
- 3.
- 4.
- 5.
- 6.
- 7.
- 8.
- 9.
- 10.
- 11.
- 12.
References
Balog, K., Azzopardi, L., de Rijke, M.: Formal models for expert finding in enterprise Corpora. In: Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. SIGIR 2006, pp. 43–50. ACM, New York (2006)
Balog, K., de Rijke, M.: Determining expert profiles (with an application to expert finding). In: IJCAI 2007, Proceedings of the 20th International Joint Conference on Artifical Intelligence, pp. 2657–2662. Morgan Kaufmann Publishers Inc., San Francisco (2007)
Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent Dirichlet allocation. J. Mach. Learn. Res. 3, 993–1022 (2003)
Bradley, A.P.: The use of the area under the ROC curve in the evaluation of machine learning algorithms. Pattern Recogn. 30(7), 1145–1159 (1997)
Campbell, C.S., Maglio, P.P., Cozzi, A., Dom, B.: Expertise identification using email communications. In: Proceedings of the Twelfth International Conference on Information and Knowledge Management, CIKM 2003, pp. 528–531. ACM, New York (2003)
Craswell, N., Hawking, D., Vercoustre, A.-M., Wilkins, P.: P@noptic expert: searching for experts not just for documents. In: Ausweb, pp. 21–25 (2001)
Davenport, T.H., Prusak, L., Prusak, L.: Working Knowledge: How Organizations Manage What They Know. Harvard Business School Press, Boston (1997)
de La Robertie, B., Pitarch, Y., Teste, O.: Measuring article quality in Wikipedia using the collaboration network. In: Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2015, ASONAM 2015, pp. 464–471. ACM, New York (2015)
Deng, H., King, I., Lyu, M.R.: Formal models for expert finding on DBLP bibliography data. In: Proceedings of the 2008 Eighth IEEE International Conference on Data Mining, ICDM 2008, pp. 163–172. IEEE Computer Society, Washington, D.C. (2008)
Gollapalli, S.D., Mitra, P., Giles, C.L.: Ranking experts using author-document-topic graphs. In: Proceedings of the 13th ACM/IEEE-CS Joint Conference on Digital Libraries. JCDL 2013, pp. 87–96. ACM, New York (2013)
Haveliwala, T.H.: Topic-sensitive pagerank. In: Proceedings of the 11th International Conference on World Wide Web, WWW 2002, pp. 517–526. ACM, New York (2002)
Huynh, T., Takasu, A., Masada, T., Hoang, K.: Collaborator recommendation for isolated researchers. In: Proceedings of the 2014 28th International Conference on Advanced Information Networking and Applications Workshops, WAINA 2014, pp. 639–644. IEEE Computer Society, Washington, D.C. (2014)
Jurczyk, P., Agichtein, E.: Discovering authorities in question answer communities by using link analysis. In: Proceedings of the Sixteenth ACM Conference on Conference on Information and Knowledge Management, CIKM 2007, pp. 919–922. ACM, New York (2007)
Kleinberg, J.M.: Authoritative sources in a hyperlinked environment. J. ACM 46(5), 604–632 (1999)
Lee, D., Seung, H.: Algorithms for non-negative matrix factorization. Adv. Neural Inf. Process. Syst. 1, 556–562 (2001)
Li, C.-L., Su, Y.-C., Lin, T.-W., Tsai, C.-H., Chang, W.-C., Huang, K.-H., Kuo, T.-M., Lin, S.-W., Lin, Y.-S., Lu, Y.-C., Yang, C.-P., Chang, C.-X., Chin, W.-S., Juan, Y.-C., Tung, H.-Y., Wang, J.-P., Wei, C.-K., Wu, F., Yin, T.-C., Yu, T., Zhuang, Y., Lin, S.-D., Lin, H.-T., Lin, C.-J.: Combination of feature engineering and ranking models for paper-author identification in KDD cup 2013. In: Proceedings of the 2013 KDD Cup 2013 Workshop, KDD Cup 2013, pp. 2:1–2:7. ACM, New York (2013)
Nocedal, J.: Updating quasi-Newton matrices with limited storage. Math. Comput. 35(151), 773–782 (1980)
Page, L., Brin, S., Motwani, R., Winograd, T.: The pagerank citation ranking: bringing order to the web. In: Proceedings of the 7th International World Wide Web Conference, pp. 161–172 (1998)
Rybak, J., Balog, K., Nørvåg, K.: Temporal expertise profiling. In: Proceedings of the 36th European Conference on Advances in Information Retrieval, ECIR 2014, pp. 540–546 (2014)
Serdyukov, P., Taylor, M., Vinay, V., Richardson, M., White, R.W.: Automatic people tagging for expertise profiling in the enterprise. In: Proceedings of the 33rd European Conference on Advances in Information Retrieval, ECIR 2011 (2011)
Tang, J., Yao, L., Zhang, D., Zhang, J.: A combination approach to web user profiling. ACM Trans. Knowl. Discov. Data 5(1), 2:1–2:44 (2010)
Tang, J., Zhang, J., Jin, R., Yang, Z., Cai, K., Zhang, L., Su, Z.: Topic level expertise search over heterogeneous networks. Mach. Learn. 82(2), 211–237 (2011)
White, S., Smyth, P.: Algorithms for estimating relative importance in networks. In: Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2003, pp. 266–275. ACM, New York (2003)
Yang, Z., Tang, J., Wang, B., Guo, J., Li, J., Chen, S.: Expert2bole: from expert finding to bole search. In: Knowledge Discovery and Data Mining (2009)
Yimam-Seid, D., Kobsa, A.: Expert-finding systems for organizations: problem and domain analysis and the DEMOIR approach. J. Org. Comput. Electron. Commer. 13(1), 1–24 (2003)
Zhang, J., Ackerman, M.S., Adamic, L.: Expertise networks in online communities: structure and algorithms. In: Proceedings of the 16th International Conference on World Wide Web, WWW 2007, pp. 221–230. ACM, New York (2007)
Zhou, D., Zhu, S., Yu, K., Song, X., Tseng, B.L., Zha, H., Giles, C.L.: Learning multiple graphs for document recommendations. In: Proceedings of the 17th International Conference on World Wide Web, WWW 2008, pp. 141–150. ACM, New York (2008)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
de La Robertie, B., Ermakova, L., Pitarch, Y., Takasu, A., Teste, O. (2017). A Unified Approach for Learning Expertise and Authority in Digital Libraries. In: Candan, S., Chen, L., Pedersen, T., Chang, L., Hua, W. (eds) Database Systems for Advanced Applications. DASFAA 2017. Lecture Notes in Computer Science(), vol 10178. Springer, Cham. https://doi.org/10.1007/978-3-319-55699-4_22
Download citation
DOI: https://doi.org/10.1007/978-3-319-55699-4_22
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-55698-7
Online ISBN: 978-3-319-55699-4
eBook Packages: Computer ScienceComputer Science (R0)