research-article

On Tag Recommendation for Expertise Profiling: A Case Study in the Scientific Domain

Authors:

Isac S. Ribeiro,

Rodrygo L.T. Santos,

Marcos A. Gonçalves,

Alberto H.F. LaenderAuthors Info & Claims

WSDM '15: Proceedings of the Eighth ACM International Conference on Web Search and Data Mining

Pages 189 - 198

https://doi.org/10.1145/2684822.2685320

Published: 02 February 2015 Publication History

Abstract

Building expertise profiles is a crucial step towards identifying experts in different knowledge areas. However, summarizing the topics of expertise of a given individual is a challenging task, primarily due to the semi-structured and heterogeneous nature of the documentary evidence available for this task. In this paper, we investigate the suitability of tag recommendation as a mechanism to produce effective expertise profiles. In particular, we perform a large-scale user study with academic experts from different knowledge areas to assess the effectiveness of multiple supervised and unsupervised tag recommendation approaches as well as multiple sources of textual evidence. Our analysis reveals that traditional content-based tag recommenders perform well at identifying expertise-oriented tags, with article keywords being a particularly effective source of evidence across profiles in different knowledge areas and with various levels of sparsity. Moreover, by combining multiple recommenders and sources of evidence as learning signals, we further demonstrate the effectiveness of tag recommendation for expertise profiling.

References

[1]

R. A. Baeza-Yates and B. Ribeiro-Neto. Modern Information Retrieval. 2 edition, 2011.

Digital Library

[2]

K. Balog and M. de Rijke. Determining Expert Profiles (With an Application to Expert Finding). In Proc. of JCDL, pages 2657--2662, 2007.

Digital Library

[3]

K. Balog, Y. Fang, M. de Rijke, P. Serdyukov, and L. Si. Expertise retrieval. FTIR, 6(2-3):127--256, 2012.

Digital Library

[4]

F. Belém, E. Martins, T. Pontes, J. Almeida, and M. Gonçalves. Associative Tag Recommendation Exploiting Multiple Textual Features. In Proc. of SIGIR, pages 1033--1041, 2011.

Digital Library

[5]

F. Belém, R. Santos, M. Gonçalves, and J. Almeida. Topic Diversity in Tag Recommendation. In Proc. of RecSys, 2013.

Digital Library

[6]

F. M. Belém, E. F. Martins, J. M. Almeida, and M. A. Gonçalves. Personalized and object-centered tag recommendation methods for Web 2.0 applications. Inf. Process. Manage., 50(4):524--553, 2014.

[7]

B. Bi and J. Cho. Automatically Generating Descriptions for Resources by Tag Modeling. In Proc. of CIKM, 2013.

Digital Library

[8]

L. Breiman. Random forests. Machine learning, 45(1):5--32, 2001.

Digital Library

[9]

C. Burges, T. Shaked, E. Renshaw, A. Lazier, M. Deeds, N. Hamilton, and G. Hullender. Learning to Rank Using Gradient Descent. In Proc. of the ICML, pages 89--96. ACM, 2005.

Digital Library

[10]

S. Canuto, F. M. Belém, J. M. Almeida, and M. A. Gonçalves. A Comparative Study of Learning-to-Rank Techniques for Tag Recommendation. JIDM, 4(3):453--468, 2013.

[11]

Z. Cao, T. Qin, T.-Y. Liu, M.-F. Tsai, and H. Li. Learning to Rank: from Pairwise Approach to Listwise Approach. In Proc. of ICML, pages 129--136. ACM, 2007.

Digital Library

[12]

M. de Rijke, K. Balog, T. Bogers, and A. van den Bosch. On the Evaluation of Entity Profiles. In Proc. of CLEF, pages 94--99, 2010.

Digital Library

[13]

M. D. Ekstrand, J. T. Riedl, and J. A. Konstan. Collaborative filtering recommender systems. Found. Trends Hum.-Comput. Interact., 4(2):81--173, 2011.

Digital Library

[14]

Y. Fang and A. Godavarthy. Modeling the Dynamics of Personal Expertise. In Proc. of SIGIR, pages 1107--1110, 2014.

Digital Library

[15]

F. Figueiredo, F. Belém, H. Pinto, J. Almeida, M. Gonçalves, D. Fernandes, E. Moura, and M. Cristo. Evidence of Quality of Textual Features on the Web 2.0. In Proc. of CIKM, pages 909--918, 2009.

Digital Library

[16]

Y. Freund, R. Iyer, R. E. Schapire, and Y. Singer. An efficient boosting algorithm for combining preferences. JMLR, 4:933--969, 2003.

Digital Library

[17]

J. H. Friedman. Greedy function approximation: a gradient boosting machine. AOS, pages 1189--1232, 2001.

[18]

N. Garg and I. Weber. Personalized, Interactive Tag Recommendation for Flickr. In Proc. of RecSys, pages 67--74, 2008.

Digital Library

[19]

P. Heymann, D. Ramage, and H. Garcia-Molina. Social Tag Prediction. In Proc. of SIGIR, pages 531--538, 2008.

Digital Library

[20]

J. Lane. Let's make science metrics more scientific. Nature, 464(7288):488--489, 2010.

[21]

Z. Lin, G. Ding, M. Hu, J. Wang, and J. Sun. Automatic Image Annotation Using Tag-Related Random Search Over Visual Neighbors. In Proc. of CIKM, pages 1784--1788, 2012.

Digital Library

[22]

C. D. Manning and H. Schütze. Foundations of Statistical Natural Language Processing. 1999.

Digital Library

[23]

E. F. Martins, F. M. Belém, J. M. Almeida, and M. A. Gonçalves. On cold start for associative tag recoomendation. JASIST (accepted for publication), 2014.

[24]

D. Metzler and W. B. Croft. Linear feature-based models for information retrieval. Information Retrieval, 10(3):257--274, 2007.

Digital Library

[25]

R. Prokofyev, A. Boyarsky, O. Ruchayskiy, K. Aberer, G. Demartini, and P. Cudré-Mauroux. Tag Recommendation for Large-scale Ontology-based Information Systems. In Proc. of ISWC, 2012.

Digital Library

[26]

C. Quoc and V. Le. Learning to rank with nonsmooth cost functions. NIPS, 19:193, 2007.

[27]

J. Rybak, K. Balog, and K. Nørvåg. Temporal Expertise Profiling. In Proc. of ECIR, pages 540--546. 2014.

[28]

P. Serdyukov, M. Taylor, V. Vinay, M. Richardson, and R. W. White. Automatic People Tagging for Expertise Profiling in the Enterprise. In Proc. of ECIR, pages 399--410, 2011.

Digital Library

[29]

S. Siersdorfer, J. S. Pedro, and M. Sanderson. Automatic Video Tagging Using Content Redundancy. In Proc. of SIGIR, pages 395--402, 2009.

Digital Library

[30]

B. Sigurbjörnsson and R. van Zwol. Flickr Tag Recommendation Based on Collective Knowledge. In Proc. of WWW, pages 327--336, 2008.

Digital Library

[31]

B. Sigurbjörnsson and R. van Zwol. Flickr Tag Recommendation Based on Collective Knowledge. In Proc. 17th International Conference on World Wide Web (WWW), pages 327--336, 2008.

Digital Library

[32]

Y. Song, L. Zhang, and C. Giles. Automatic tag recommendation algorithms for social recommender systems. TWEB, 5:1--31, 2011.

Digital Library

[33]

P. Venetis, G. Koutrika, and H. Garcia-Molina. On the Selection of Tags for Tag Clouds. In Proc. of WSDM, pages 835--844, 2011.

Digital Library

[34]

L. Wu, L. Yang, N. Yu, and X.-S. Hua. Learning to Tag. In Proc. of WWW, pages 361--370, 2009.

Digital Library

[35]

Q. Wu, C. J. Burges, K. M. Svore, and J. Gao. Adapting boosting for information retrieval measures. Information Retrieval, 13(3):254--270, 2010.

Digital Library

[36]

J. Xu and H. Li. Adarank: a Boosting Algorithm for Information Retrieval. In Proc. of SIGIR, pages 391--398. ACM, 2007.

Digital Library

[37]

Y. Yang and J. O. Pedersen. A Comparative Study on Feature Selection in Text Categorization. In Proc. of the ICML, pages 412--420, 1997.

Digital Library

[38]

D. Yin, S. Guo, B. Chidlovskii, B. Davison, C. Archambeau, and G. Bouchard. Connecting Comments and Tags: Improved Modeling of Social Tagging Systems. In Proc. of WSDM, pages 547--556, 2013.

Digital Library

Cited By

M. Belém FM. Almeida JA. Gonçalves M(2020)Tagging and Tag RecommendationCyberspace10.5772/intechopen.82242Online publication date: 17-Jun-2020
https://doi.org/10.5772/intechopen.82242
Hafidi MAbdelwahed EQassimi S(2020)Graph‐based tag recommendations using clusters of patients in clinical decision support systemConcurrency and Computation: Practice and Experience10.1002/cpe.562433:1Online publication date: 6-Jan-2020
https://doi.org/10.1002/cpe.5624
Husain OSalim NAlias RAbdelsalam SHassan A(2019)Expert Finding Systems: A Systematic ReviewApplied Sciences10.3390/app92042509:20(4250)Online publication date: 11-Oct-2019
https://doi.org/10.3390/app9204250
Show More Cited By

Index Terms

On Tag Recommendation for Expertise Profiling: A Case Study in the Scientific Domain
1. Information systems
  1. Information retrieval
    1. Retrieval models and ranking
    2. Users and interactive retrieval
      1. Personalization
  2. World Wide Web
    1. Web searching and information discovery
      1. Personalization

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cover image ACM Conferences

WSDM '15: Proceedings of the Eighth ACM International Conference on Web Search and Data Mining

February 2015

482 pages

ISBN:9781450333177

DOI:10.1145/2684822

General Chairs:
Xueqi Cheng
ICT, Chinese Academy of Sciences, China
,
Hang Li
Huawei Technologies, China
,
Program Chairs:
Evgeniy Gabrilovich
Google, USA
,
Jie Tang
Tsinghua University, China

Copyright © 2015 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 02 February 2015

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Conselho Nacional de Desenvolvimento Científico e Tecnológico

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WSDM 2015

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WSDM 2015: Eighth ACM International Conference on Web Search and Data Mining

February 2 - 6, 2015

Shanghai, China

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WSDM '15 Paper Acceptance Rate 39 of 238 submissions, 16%;

Overall Acceptance Rate 498 of 2,863 submissions, 17%

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Cited By

M. Belém FM. Almeida JA. Gonçalves M(2020)Tagging and Tag RecommendationCyberspace10.5772/intechopen.82242Online publication date: 17-Jun-2020
https://doi.org/10.5772/intechopen.82242
Hafidi MAbdelwahed EQassimi S(2020)Graph‐based tag recommendations using clusters of patients in clinical decision support systemConcurrency and Computation: Practice and Experience10.1002/cpe.562433:1Online publication date: 6-Jan-2020
https://doi.org/10.1002/cpe.5624
Husain OSalim NAlias RAbdelsalam SHassan A(2019)Expert Finding Systems: A Systematic ReviewApplied Sciences10.3390/app92042509:20(4250)Online publication date: 11-Oct-2019
https://doi.org/10.3390/app9204250
Gonçalves RDorneles C(2019)Automated Expertise RetrievalACM Computing Surveys10.1145/333100052:5(1-30)Online publication date: 13-Sep-2019
https://dl.acm.org/doi/10.1145/3331000
Du GLiu YYu J(2018)Scientific Users' Interest Detection and Collaborators Recommendation2018 IEEE Fourth International Conference on Big Data Computing Service and Applications (BigDataService)10.1109/BigDataService.2018.00019(72-79)Online publication date: Mar-2018
https://doi.org/10.1109/BigDataService.2018.00019
Tran HCabanac GHubert G(2017)Expert suggestion for conference program committees2017 11th International Conference on Research Challenges in Information Science (RCIS)10.1109/RCIS.2017.7956540(221-232)Online publication date: May-2017
https://doi.org/10.1109/RCIS.2017.7956540
Osuna FAkbar MGates A(2017)On Using Disparate Scholarly Data to Identify Potential Members for Interdisciplinary Research Groups2017 IEEE International Conference on Information Reuse and Integration (IRI)10.1109/IRI.2017.33(59-68)Online publication date: Aug-2017
https://doi.org/10.1109/IRI.2017.33
de Siqueira GCanuto SGonçalves MLaender A(2017)Automatic Hierarchical Categorization of Research Expertise Using Minimum InformationResearch and Advanced Technology for Digital Libraries10.1007/978-3-319-67008-9_9(103-115)Online publication date: 2-Sep-2017
https://doi.org/10.1007/978-3-319-67008-9_9
Belém FAlmeida JGonçalves M(2017)A survey on tag recommendation methodsJournal of the Association for Information Science and Technology10.1002/asi.2373668:4(830-844)Online publication date: 1-Apr-2017
https://dl.acm.org/doi/10.1002/asi.23736
Mangaravite VSantos RRibeiro IGonçalves MLaender APerego RSebastiani FAslam JRuthven IZobel J(2016)The LExR Collection for Expertise Retrieval in AcademiaProceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval10.1145/2911451.2914678(721-724)Online publication date: 7-Jul-2016
https://dl.acm.org/doi/10.1145/2911451.2914678

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