Skip to main content
SpringerLink
Log in

Exploration of methodologies to improve job recommender systems on social networks

  • Original Article
  • Published:
Social Network Analysis and Mining Aims and scope Submit manuscript

Abstract

This paper presents content-based recommender systems which propose relevant jobs to Facebook and LinkedIn users. These systems have been developed at Work4, the Global Leader in Social and Mobile Recruiting. The profile of a social network user contains two types of data: user data and user friend data; furthermore, the profile of our users and the description of our jobs consist of text fields. The first experiments suggest that to predict the interests of users for jobs using basic similarity measures together with data collected by Work4 can be improved upon. The next experiments then propose a method to estimate the importance of users’ and jobs’ different fields in the task of job recommendation; taking into account these weights allow us to significantly improve the recommendations. The third part of this paper analyzes social recommendation approaches, validating the suitability for job recommendations for Facebook and LinkedIn users. The last experiments focus on machine learning algorithms to improve the obtained results with basic similarity measures. Support vector machines (SVM) shows that supervised learning procedure increases the performance of our content-based recommender systems; it yields best results in terms of AUC in comparison with other investigated methodologies such as Matrix Factorization and Collaborative Topic Regression.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

Notes

  1. http://www.kaggle.com/c/msdchallenge.

  2. http://www.onetcenter.org/taxonomy.html.

  3. ANRT: Association Nationale de la Recherche et de la Technologie.

References

  • Adomavicius G, Tuzhilin A (2005) Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. IEEE Trans Knowl Data Eng 17(6): 734–749

  • Aiolli F (2013) Efficient top-n recommendation for very large scale binary rated datasets. In: Proceedings of the 7th ACM Conference on Recommender Systems, ser. RecSys ’13. ACM, New York, NY, USA, pp 273–280.

  • Anand A, Pugalenthi G, Fogel GB, Suganthan PN (2010) An approach for classification of highly imbalanced data using weighting and undersampling. Amino Acids 39(5):1385–91

    Article  Google Scholar 

  • Aranda J, Givoni I, Handcock J, Tarlow D (2007) An online social network-based recommendation system. Department of Computer Science-University of Toronto, Computer Sciences Technical Report

  • Balabanovic M, Shoham Y (1997) Fab: content-based, collaborative recommendation. Comm. ACM 40(3):66–72

    Article  Google Scholar 

  • Baeza-Yates RA, Berthier R-N (1999) Modern Information Retrieval. Addison-Wesley Longman Publishing Co., Inc, Boston, MA, USA

    Google Scholar 

  • Bennett J, Lanning S, Netflix N (2007) The netflix prize. In: In KDD Cup and Workshop in conjunction with KDD

  • Blei DM, Lafferty JD (2009) Topic models. In: Srivastava AN, Sahami M (Eds) CRC Press

  • Bobadilla J, Ortega F, Hernando A, Gutiérrez A (2013) Recommender systems survey. Knowl Based Systems 46:109–132

    Article  Google Scholar 

  • Boyd DM, Ellison NB (2008) Social network sites: definition, history, and scholarship. J Comput Mediat Commun 13(1):210–230

    Article  Google Scholar 

  • Brain S (2014). Available at http://www.statisticbrain.com/twitter-statistics/

  • Chang C-C, Lin C-J (2011) Libsvm: a library for support vector machines. ACM Trans Intell Syst Technol 2(3), pp 27:1–27:27. Software available at http://www.csie.ntu.edu.tw/cjlin/libsvm

  • Claypool M, Gokhale A, Miranda T, Murnikov P, Netes D, Sartin M (1999) Combining content-based and collaborative filters in an online newspaper

  • Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20(3):273–297

    MATH  Google Scholar 

  • Cover TM (1965) Geometrical and statistical properties of systems of linear inequalities with applications in pattern recognition. Electron Comput IEEE Trans, vol. EC-14, no. 3, pp 326–334

  • de Campos LM, Fernández-Luna JM, Huete JF, Rueda-Morales MA (2010) Combining content-based and collaborative recommendations: a hybrid approach based on bayesian networks. Int J Approx Reason 51(7):785–799

    Article  Google Scholar 

  • Diaby M, Viennet E, Launay T (2013) Toward the next generation of recruitment tools: an online social network-based job recommender system. In: Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining ASONAM 2013, pp 821–828.

  • Facebook (2014) Available at http://newsroom.fb.com/company-info/

  • Gao F, Xing C, Du X, Wang S (2007) Personalized service system based on hybrid filtering for digital library. Tsinghua Sci Technol 12(1):1–8

    Article  Google Scholar 

  • Golbeck J (2006) Generating predictive movie recommendations from trust in social networks. In: Proceedings of the 4th International Conference on Trust Management, ser. iTrust’06. Springer, Berlin, Heidelberg, pp 93–104.

  • Groh G, Ehmig C (2007) Recommendations in taste related domains: collaborative filtering vs. social filtering. In: Proceedings of the 2007 International ACM Conference on Supporting Group Work, ser. GROUP ’07. ACM, New York, NY, USA, pp 127–136.

  • Han J (1996) Data mining techniques. SIGMOD Rec 25(2): 545

  • Jannach D, Zanker M, Felfernig A, Friedrich G (2011) Recommender systems: an introduction. Cambridge University Press, Cambridge

  • Joachims T (1998) Text categorization with suport vector machines: learning with many relevant features. In: Proceedings of the 10th European Conference on Machine Learning, ser. ECML ’98. Springer, London, UK, pp 137–142.

  • Kantor PB (2009) Recommender systems handbook. Springer, New York; London

    Google Scholar 

  • Kazienko P, Musiał K, Kajdanowicz T (2011) Multidimensional social network in the social recommender system, vol. 41, no. 4, pp. 746–759

  • Lemire D, Maclachlan A (2005) Slope one predictors for online rating-based collaborative filtering. In: Proceedings of SIAM Data Mining (SDM’05)

  • Linden G, Smith B, York J (2003) Amazon.com recommendations: item-to-item collaborative filtering. IEEE Internet Comput 7(1):76–80

    Article  Google Scholar 

  • LinkedIn (2014). Available at http://press.linkedin.com/about

  • Lops P, de Gemmis M, Semeraro G (2011) Content-based recommender systems: state of the art and trends. In: Ricci F, Rokach L, Shapira B, Kantor PB (Eds) Recommender systems handbook. Springer, Berlin, pp 73–105.

  • Ma H, Zhou D, Liu C, Lyu MR, King I (2011) Recommender systems with social regularization. In: Proceedings of the fourth ACM international conference on Web search and data mining, ser. WSDM ’11. ACM, New York, NY, USA, pp 287–296.

  • Nicholas ISC, Nicholas CK (1999) Combining content and collaboration in text filtering. In: Proceedings of the IJCAI 99 Workshop on machine learning for information filtering, pp 86–91.

  • Omary Z, Mtenzi F (2010) Machine learning approach to identifying the dataset threshold for the performance estimators in supervised learning. Int J Infon (IJI) 3

  • Pazzani MJ, Billsus D (2007) The adaptive web. In: Brusilovsky P, Kobsa A, Nejdl W (Eds) Content-based Recommendation Systems. Springer, Berlin, Heidelberg, pp 325–341.

  • Pazzani M, Billsus D (1997) Learning and revising user profiles: the identification of interesting web sites. Mach Learn 27:313–331

    Article  Google Scholar 

  • Ravikumar P, Tewari A, Yang E (2011) On ndcg consistency of listwise ranking methods. Available at http://www.cs.utexas.edu/users/ai-lab/?RTY11

  • Rijsbergen CJV (1979) Information Retrieval, 2nd edn. Butterworth-Heinemann, Newton, MA, USA

    Google Scholar 

  • Rocchio JJ (1971) Relevance feedback in information retrieval. In: Salton G (Ed) The SMART retrieval system: experiments in automatic document processing, ser. Prentice-Hall Series in Automatic Computation. Prentice-Hall, Englewood Cliffs NJ, ch. 14, pp 313–323.

  • Salakhutdinov R, Mnih A (2008a) Bayesian probabilistic matrix factorization using markov chain monte carlo

  • Salakhutdinov R, Mnih A (2008b) Probabilistic matrix factorization

  • Salton G, Wang A, Yang C (1975) A vector space model for information retrieval. J Am Soc Inf Sci 18(11):613–620

    MATH  Google Scholar 

  • Séguela J (2012) Fouille de données textuelles et systèmes de recommandation appliqués aux offres d’emploi diffusées sur le web. Ph.D. dissertation, Conservatoire National des Arts et Métiers (CNAM), Paris, France

  • Shardanand U, Maes P (1995) Social information filtering: algorithms for automating “word of mouth”. In: Proceedings of the SIGCHI Conference on human factors in computing systems, ser. CHI ’95. ACM Press/Addison-Wesley Publishing Co., New York, NY, USA, pp 210–217.

  • Wang C, Blei DM (2011) Collaborative topic modeling for recommending scientific articles. In: Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining, ser. KDD ’11. New York, NY, USA: ACM, pp 448–456.

  • Xiao B, Benbasat I (2007) E-commerce product recommendation agents: use, characteristics, and impact. In: MIS Quarterly, vol. 31, no. 1. Society for Information Management and The Management Information Systems Research Center Minneapolis, MN, USA, pp 137–209.

Download references

Acknowledgments

This work is supported by Work4, ANRT\(^{3}\) (the French National Research and Technology Association), the French FUI Project AMMICO and the project Open Food System. The authors thank Guillaume Leseur, software architect at Work4, Benjamin Combourieu and all the Work4Engines team. We thank all the anonymous reviewers who spent their time and energy reviewing this paper, thanks for your reviews and helpful comments and remarks.

Author information

Authors and Affiliations

  1. Université Paris 13, Sorbonne Paris Cité, L2TI, 93430, Villetaneuse, France

    Mamadou Diaby & Emmanuel Viennet

  2. R&D Department of Work4, 3 Rue Moncey, 75009, Paris, France

    Mamadou Diaby & Tristan Launay

Authors
  1. Mamadou Diaby
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Emmanuel Viennet
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Tristan Launay
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mamadou Diaby.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Diaby, M., Viennet, E. & Launay, T. Exploration of methodologies to improve job recommender systems on social networks. Soc. Netw. Anal. Min. 4, 227 (2014). https://doi.org/10.1007/s13278-014-0227-z

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s13278-014-0227-z

Keywords

Search

Navigation