Exploration of methodologies to improve job recommender systems on social networks

Social Network Analysis and Mining

Volumn 4, Issue 1, 2014, Pages 1-17

  Diaby, Mamadou ^a,b   Viennet, Emmanuel ^a   Launay, Tristan ^b  

^a UNIVERSITÉ PARIS 13   (France)

^b R D Department of Work4   (France)

Author keywords

Content based recommender system; Facebook; Job recommendation; LinkedIn; Social recommender systems; SVM

Indexed keywords

FACTORIZATION; LEARNING ALGORITHMS; LEARNING SYSTEMS; RECOMMENDER SYSTEMS; SUPPORT VECTOR MACHINES;

CONTENT-BASED RECOMMENDER SYSTEMS; FACEBOOK; JOB RECOMMENDATION; LINKEDIN; SOCIAL RECOMMENDER SYSTEMS;

SOCIAL NETWORKING (ONLINE);

EID: 84947235091     PISSN: 18695450     EISSN: 18695469     Source Type: Journal    
DOI: 10.1007/s13278-014-0227-z     Document Type: Article

References (45)

1. 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
2. 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.
3. 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
4. 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
5. Balabanovic M, Shoham Y (1997) Fab: content-based, collaborative recommendation. Comm. ACM 40(3):66–72
6. Baeza-Yates RA, Berthier R-N (1999) Modern Information Retrieval. Addison-Wesley Longman Publishing Co., Inc, Boston, MA, USA
7. Bennett J, Lanning S, Netflix N (2007) The netflix prize. In: In KDD Cup and Workshop in conjunction with KDD
8. Blei DM, Lafferty JD (2009) Topic models. In: Srivastava AN, Sahami M (Eds) CRC Press
9. Bobadilla J, Ortega F, Hernando A, Gutiérrez A (2013) Recommender systems survey. Knowl Based Systems 46:109–132
10. Boyd DM, Ellison NB (2008) Social network sites: definition, history, and scholarship. J Comput Mediat Commun 13(1):210–230
11. Brain S (2014). Available at http://www.statisticbrain.com/twitter-statistics/
12. 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
13. Claypool M, Gokhale A, Miranda T, Murnikov P, Netes D, Sartin M (1999) Combining content-based and collaborative filters in an online newspaper
14. Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20(3):273–297
15. 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
16. 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
17. 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.
18. Facebook (2014) Available at http://newsroom.fb.com/company-info/
19. 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
20. 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.
21. 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.
22. Han J (1996) Data mining techniques. SIGMOD Rec 25(2): 545
23. Jannach D, Zanker M, Felfernig A, Friedrich G (2011) Recommender systems: an introduction. Cambridge University Press, Cambridge
24. 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.
25. Kantor PB (2009) Recommender systems handbook. Springer, New York; London
26. Kazienko P, Musiał K, Kajdanowicz T (2011) Multidimensional social network in the social recommender system, vol. 41, no. 4, pp. 746–759
27. Lemire D, Maclachlan A (2005) Slope one predictors for online rating-based collaborative filtering. In: Proceedings of SIAM Data Mining (SDM’05)
28. Linden G, Smith B, York J (2003) Amazon.com recommendations: item-to-item collaborative filtering. IEEE Internet Comput 7(1):76–80
29. LinkedIn (2014). Available at http://press.linkedin.com/about
30. 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.
31. 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.
32. 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.
33. 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
34. 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.
35. Pazzani M, Billsus D (1997) Learning and revising user profiles: the identification of interesting web sites. Mach Learn 27:313–331
36. 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
37. Rijsbergen CJV (1979) Information Retrieval, 2nd edn. Butterworth-Heinemann, Newton, MA, USA
38. 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.
39. Salakhutdinov R, Mnih A (2008a) Bayesian probabilistic matrix factorization using markov chain monte carlo
40. Salakhutdinov R, Mnih A (2008b) Probabilistic matrix factorization
41. Salton G, Wang A, Yang C (1975) A vector space model for information retrieval. J Am Soc Inf Sci 18(11):613–620
42. 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
43. 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.
44. 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.
45. 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.