Predicting Human Preferences Using the Block Structure of Complex Social Networks

PLoS ONE

Volumn 7, Issue 9, 2012, Pages

  Guimerà, Roger ^a,b   Llorente, Alejandro ^c   Moro, Esteban ^c,d,e   Sales Pardo, Marta ^b  

^a INSTITUCIÓ CATALANA DE RECERCA I ESTUDIS AVANÇATS ICREA   (Spain)

^b UNIVERSITAT ROVIRA I VIRGILI   (Spain)

^c UNIVERSIDAD AUTÓNOMA DE MADRID   (Spain)

^d UNIVERSIDAD CARLOS III DE MADRID   (Spain)

^e INSTITUTO DE CIENCIAS MATEMÁTICAS   (Spain)

Author keywords

[No Author keywords available]

Indexed keywords

ACCURACY; ALGORITHM; ARTICLE; BAYESIAN LEARNING; BEHAVIOR; CONTROLLED STUDY; DECISION MAKING; PREDICTION; PREFERENCE; RATING SCALE; SOCIAL INTERACTION; SOCIAL NETWORK; STOCHASTIC BLOCK MODEL; STOCHASTIC MODEL;

ALGORITHMS; BAYES THEOREM; CHOICE BEHAVIOR; DATA COLLECTION; HUMANS; MODELS, STATISTICAL; MOTION PICTURES AS TOPIC; SOCIAL SUPPORT; STOCHASTIC PROCESSES;

EID: 84866276354     PISSN: None     EISSN: 19326203     Source Type: Journal    
DOI: 10.1371/journal.pone.0044620     Document Type: Article

References (33)

1. (2010) The data deluge. The Economist Feb. 25th.
2. Adomavicius G, Tuzhilin A, (2005) Towards the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE T Knowl Data En 17: 734-749.
3. Melville P, Sindhwani V (2010) Encyclopedia of Machine Learning, Springer-Verlag, chapter Recommender systems.
4. Ekstrand MD, Ludwig M, Konstan JA, Riedl JT (2011) Rethinking the recommender research ecosystem: reproducibility, openness, and lenskit. Proceedings of the fifth ACM Conference on Recommender Systems: 133-140.
5. Watts DJ, (2007) A twenty-first century science. Nature 445: 489.
6. Lazer D, Pentland A, Adamic L, Aral S, Barabási AL, et al. (2009) Computational social science. Science 323: 721-723.
7. Brockmann D, Hufnagel L, Gaisel T, (2006) The scaling laws of human travel. Nature 439: 462-465.
8. Song C, Qu Z, Blumm N, Barabási AL, (2010) Limits of predictability in human mobility. Science 327: 1018-1021.
9. Malmgren RD, Stouffer DB, Campanharo ASLO, Amaral LA, (2009) On universality in human correspondence activity. Science 325: 1696-1700.
10. Salganik MJ, Dodds PS, Watts DJ, (2006) Experimental study of inequality and unpredictability in an artificial cultural market. Science 311: 854-856.
11. Christakis N, Fowler J, (2007) The spread of obesity in a large social network over 32 years. New Engl J Med 357: 370-379.
12. Cohen-Cole E, Fletcher J, (2008) Is obesity contagious? Social networks vs. environmental factors in the obesity epidemic. J Health Econ 27: 1382-1387.
13. Fowler J, Christakis N, (2008) Estimating peer effects on health in social networks: A response to Cohen-Cole and Fletcher; and Trogdon, Nonnemaker, Pais. J Health Econ 27: 1400.
14. Vespignani A, (2009) Predicting the behavior of techno-social systems. Science 325: 425-428.
15. Iribarren JL, Moro E, (2009) Impact of human activity patterns on the dynamics of information diffusion. Phys Rev Lett 103: 038702.
16. Su X, Khoshgoftaar TM, (2009) A survey of collaborative filtering techniques. Adv in Artif Intell 2009: 4 2-4: 2.
17. Koren Y, Bell R, Volinsky C, (2009) Matrix factorization techniques for recommender systems. Computer 42: 30-37.
18. Breiger RL, Boorman SA, Arabie P, (1975) An algorithm for clustering relational data with applications to social network analysis and comparison with multidimensional scaling. J Math Psychol 12: 328-383.
19. White HC, Boorman SA, Breiger RL, (1976) Social structure from multiple networks. i. blockmodels of roles and positions. Am J Sociol 81: 730-780.
20. Holland PW, Laskey KB, Leinhardt S, (1983) Stochastic blockmodels: First steps. Soc Networks 5: 109-137.
21. Nowicki K, Snijders TAB, (2001) Estimation and prediction for stochastic blockstructures. J Am Stat Assoc 96: 1077-1087.
22. Guimerà R, Sales-Pardo M, (2009) Missing and spurious interactions and the reconstruction of complex networks. Proc Natl Acad Sci U S A 106: 22073-22078.
23. Decelle A, Krzakala F, Moore C, Zdeborová L, (2011) Inference and phase transitions in the detection of modules in sparse networks. Phys Rev Lett 107: 065701.
24. Newman MEJ, (2011) Communities, modules and large-scale structure in networks. Nat Phys 8: 25-31.
25. Jaynes ET (2003) Probability Theory: The Logic of Science. Cambridge University Press.
26. Hoeting JA, Madigan D, Raftery AE, Volinsky CT, (1999) Bayesian model averaging: A tutorial. Statistical Science 14: 382417.
27. Metropolis N, Rosenbluth AW, Rosenbluth MN, Teller AH, Teller E, (1953) Equation of state calculations by fast computing machines. J Chem Phys 21: 1087-1092.
28. Paterek A (2007) Improving regularized singular value decomposition for collaborative filtering. In: Proc. KDD Cup Workshop at SIGKDD'07, 13th ACM Int. Conf. on Knowledge Discovery and Data Mining. 39-42.
29. Sarwar B, Karypis G, Konstan J, Riedl J (2001) Item-based collaborative filtering recommendation algorithms. In: Proceedings of the 10th international conference on World Wide Web. New York, NY, USA: ACM, WWW '01, 285-295. doi:10.1145/371920.372071.
30. Sales-Pardo M, Guimerà R, Moreira AA, Amaral LAN, (2007) Extracting the hierarchical organization of complex systems. Proc Natl Acad Sci USA 104: 15224-15229.
31. Yedidia JS, Freeman WT, Weiss Y (2003) Exploring artificial intelligence in the new millennium. San Francisco, CA, USA: Morgan Kaufmann Publishers Inc., chapter Understanding belief prop-agation and its generalizations. 239-269.
32. Malmgren RD, Hofman JM, Amaral LAN, Watts DJ (2009) Characterizing individual communication patterns. In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 607-615.
33. Gardner W, (2003) Learning characteristics of stochastic-gradient-descent algorithms: A general study, analysis, and critique. Signal Process 6: 113-133.