Advertisement

Towards more effective consumer steering via network analysis

  • Jacopo ArpettiEmail author
  • Antonio Iovanella
Article

Abstract

Increased data gathering capacity, together with the spread of data analytics techniques, has prompterd an unprecedented concentration of information related to the individuals’ preferences in the hands of a few gatekeepers. In the present paper, we show how platforms’ performances still appear astonishing in relation to some unexplored data and networks properties, capable to enhance the platforms’ capacity to implement steering practices by means of an increased ability to estimate individuals’ preferences. To this end, we rely on network science whose analytical tools allow data representations capable of highlighting relationships between subjects and/or items, extracting a great amount of information. We therefore propose a measure called Network Information Patrimony, considering the amount of information available within the system and we look into how platforms could exploit data stemming from connected profiles within a network, with a view to obtaining competitive advantages. Our measure takes into account the quality of the connections among nodes as the one of a hypothetical user in relation to its neighbourhood, detecting how users with a good neighbourhood—hence of a superior connections set—obtain better information. We tested our measures on Amazons’ instances, obtaining evidence which confirm the relevance of information extracted from nodes’ neighbourhood in order to steer targeted users.

Keywords

Data value Network-driven economy Steering Networks theory Nearest neighbour degree 

JEL Classification

D83 D85 L11 

Notes

Acknowledgements

We would like to thank the anonymous reviewers for all their useful suggestions, as they helped us improve the quality of our paper.

References

  1. Acquisti, A. (2008). Identity management, privacy, and price discrimination. IEEE Security and Privacy, 6(2), 46–50.CrossRefGoogle Scholar
  2. Acquisti, A., Taylor, C., & Wagman, L. (2016). The economics of privacy. Journal of Economic Literature, 54(2), 442–492.CrossRefGoogle Scholar
  3. Akerlof, G. A. (1970). The market for “Lemons” quality uncertainty and the market mechanism. The Quarterly Journal of Economics, 84(3), 488–500.CrossRefGoogle Scholar
  4. Arpetti, J. (2018). Economia della privacy: Una rassegna della letteratura (in italian). Rivista di diritto dei media, 2, 267–297.Google Scholar
  5. Arrow, K. J. (1958). Utilities, attitudes, choices: A review note. Econometrica: Journal of the Econometric Society, 26, 1–23.CrossRefGoogle Scholar
  6. Bakshy, E., Rosenn, I., Marlow, C., & Adamic, L. (2012). The role of social networks in information diffusion. In Proceedings of the 21st international conference on World Wide Web, (pp. 519–528). ACMGoogle Scholar
  7. Barabási, A. L. (2013). Network science. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 371(1987), 20120375.CrossRefGoogle Scholar
  8. Barabási, A. L. (2016). Network science. Cambridge: Cambridge University Press.Google Scholar
  9. Birke, D. (2013). Social networks and their economics: Influencing consumer choice. Chichester: Wiley.CrossRefGoogle Scholar
  10. Bollobás, B. (2013). Modern graph theory (Vol. 184). New York: Springer Science & Business Media.Google Scholar
  11. Breese, J. S., Heckerman, D., & Kadie, C. (2013). Empirical analysis of predictive algorithms for collaborative filtering. Tech. rep. Microsoft Research.Google Scholar
  12. Briscoe, B., Odlyzko, A., & Tilly, B. (2006). Metcalfe’s law is wrong. IEEE Spectrum, 43(7), 34–39.CrossRefGoogle Scholar
  13. Cabral, L. M. B. (2000). Introduction to industrial organization. Cambridge: MIT Press.Google Scholar
  14. Castillejo, E., Almeida, A., & López-de Ipina, D. (2012). Social network analysis applied to recommendation systems: alleviating the cold-user problem. In International Conference on Ubiquitous Computing and Ambient Intelligence, Springer, (pp. 306–313)Google Scholar
  15. Catanzaro, M., Boguñá, M., & Pastor-Satorras, R. (2005). Generation of uncorrelated random scale-free networks. Physical Review E, 71(2), 027103.CrossRefGoogle Scholar
  16. Cerqueti, R., Ferraro, G., & Iovanella, A. (2018a). A new measure for community structures through indirect social connections. Expert Systems with Applications, 114, 196–209.CrossRefGoogle Scholar
  17. Cerqueti, R., Rotundo, G., & Ausloos, M. (2018b). Investigating the configurations in cross-shareholding: A joint copula-entropy approach. Entropy, 20(2), 134.CrossRefGoogle Scholar
  18. Competition and Markets Authority–CMA. (2015). The commercial use of consumer data report on the cma’s call for information. Competiotion and Markets Authority: Tech. rep.Google Scholar
  19. Council of Economic Advisers–CEA (2015) Big Data and Differential Pricing. Tech. rep., Council of Economic Advisers (CEA)–Executive Office of the President of the United StatesGoogle Scholar
  20. Csardi, G., Nepusz, T., et al. (2006). The igraph software package for complex network research. InterJournal, Complex Systems, 1695(5), 1–9.Google Scholar
  21. D’Agostino, G., Scala, A., Zlatić, V., & Caldarelli, G. (2012). Robustness and assortativity for diffusion-like processes in scale-free networks. EPL (Europhysics Letters), 97(6), 68006.CrossRefGoogle Scholar
  22. Erdős, P., & Gallai, T. (1960). Graphs with prescribed degrees of vertices (in hungarian). Matematikai Lapok, 11, 265–274.Google Scholar
  23. Ezrachi, A., & Stucke, M. E. (2016a). The rise of behavioural discrimination. European Competition Law Review, ECLR, 37(12), 485–492.Google Scholar
  24. Ezrachi, A., & Stucke, M. E. (2016b). Virtual competition: The promise and perils of the algorithm-driven economy. Cambridge: Harvard University Press.CrossRefGoogle Scholar
  25. Feld, S. L. (1991). Why your friends have more friends than you do. American Journal of Sociology, 96(6), 1464–1477.CrossRefGoogle Scholar
  26. Firdaus, S., & Uddin, M. A. (2015). A survey on clustering algorithms and complexity analysis. International Journal of Computer Science Issues, 12(2), 62–85.Google Scholar
  27. Fuller, C. S. (2019). Is the market for digital privacy a failure? Public Choice.CrossRefGoogle Scholar
  28. Fuller, C. S. (2018). Privacy law as price control. European Journal of Law and Economics, 45(2), 225–250.CrossRefGoogle Scholar
  29. Galati, F., Bigliardi, B., Petroni, A., Petroni, G., & Ferraro, G. (2019). A framework for avoiding knowledge leakage: Evidence from engineering to order firms. Knowledge Management Research & Practice, 17(3), 340–352.CrossRefGoogle Scholar
  30. Gertz, J. D. (2002). The purloined personality: Consumer profiling in financial services. San Diego L Rev, 39, 943.Google Scholar
  31. Gilder, G. (1993). Metcalfe’s law and legacy. Forbes ASAP, 13, 1993.Google Scholar
  32. Hakimi, S. L. (1962). On realizability of a set of integers as degrees of the vertices of a linear graph. Journal of the Society for Industrial and Applied Mathematics, 10(3), 496–506.CrossRefGoogle Scholar
  33. Hannak, A., Soeller, G., Lazer, D., Mislove, A., & Wilson, C. (2014). Measuring Price Discrimination and Steering on E-commerce Web Sites. In Proceedings of the 2014 conference on internet measurement conference–IMC ’14 (pp. 305–318). New York: ACM PressGoogle Scholar
  34. Jentzsch, N. (2017). Secondary use of personal data: A welfare analysis. European Journal of Law and Economics, 44(1), 165–192.CrossRefGoogle Scholar
  35. Kahneman, D., & Tversky, A. (1986). Rational choice and the framing of decisions. Journal of Business, 59(4), 251–278.Google Scholar
  36. Kamishima, T., & Akaho, S. (2011). Personalized pricing recommender system. In Proceedings of the 2nd international workshop on information heterogeneity and fusion in recommender systems–HetRec ’11 (pp. 57–64). New York: ACM PressGoogle Scholar
  37. Katarya, R., & Verma, O. P. (2016). A collaborative recommender system enhanced with particle swarm optimization technique. Multimedia Tools and Applications, 75(15), 9225–9239.CrossRefGoogle Scholar
  38. Konstas, I., Stathopoulos, V., & Jose, J. M. (2009). On social networks and collaborative recommendation. In Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval (pp. 195–202). ACMGoogle Scholar
  39. Krämer, A., & Kalka, R. (2017). How digital disruption changes pricing strategies and price models. In Phantom Ex Machina (pp. 87–103). SpringerGoogle Scholar
  40. Kshetri, N. (2014). Big data’s impact on privacy, security and consumer welfare. Telecommunications Policy, 38(11), 1134–1145.CrossRefGoogle Scholar
  41. Lam, C. P., & Goeksel, M. (2010). System and method for utilizing social networks for collaborative filtering. US Patent 7,689,452Google Scholar
  42. Leskovec, J., Adamic, L. A., & Huberman, B. A. (2007). The dynamics of viral marketing. ACM Transactions on the Web (TWEB), 1(1), 1–39.CrossRefGoogle Scholar
  43. Levin, J. (2011). The economics of internet markets. Tech. rep. National Bureau of Economic Research, Cambridge, MA.Google Scholar
  44. Linden, G., Smith, B., & York, J. (2003). Amazon.com recommendations: iItem-to-item collaborative filtering. IEEE Internet Computing, 7(1), 76–80.CrossRefGoogle Scholar
  45. Liu, F., & Lee, H. J. (2010). Use of social network information to enhance collaborative filtering performance. Expert Systems with Applications, 37(7), 4772–4778.CrossRefGoogle Scholar
  46. Lü, L., Medo, M., Yeung, C. H., Zhang, Y. C., Zhang, Z. K., & Zhou, T. (2012). Recommender systems. Physics Reports, 519(1), 1–49.CrossRefGoogle Scholar
  47. Lu, J., Wu, D., Mao, M., Wang, W., & Zhang, G. (2015). Recommender system application developments: A survey. Decision Support Systems, 74, 12–32.CrossRefGoogle Scholar
  48. Madureira, A., den Hartog, F., Bouwman, H., & Baken, N. (2013). Empirical validation of metcalfe’s law: How internet usage patterns have changed over time. Information Economics and Policy, 25(4), 246–256.CrossRefGoogle Scholar
  49. Mattioli, D. (2012). On orbitz, mac users steered to pricier hotels. Wall Street Journal, 23, 2012.Google Scholar
  50. Mavlanova, T., Benbunan-Fich, R., & Koufaris, M. (2012). Signaling theory and information asymmetry in online commerce. Information & Management, 49(5), 240–247.CrossRefGoogle Scholar
  51. Metcalfe, B. (2013). Metcalfe’s law after 40 years of ethernet. Computer, 46(12), 26–31.CrossRefGoogle Scholar
  52. Mikians, J., Gyarmati, L., Erramilli, V., & Laoutaris, N. (2012). Detecting price and search discrimination on the internet. In Proceedings of the 11th ACM workshop on hot topics in networks (pp. 79–84). ACMGoogle Scholar
  53. Mobasher, B., Dai, H., Luo, T., & Nakagawa, M. (2001). Improving the effectiveness of collaborative filtering on anonymous web usage data. In Proceedings of the IJCAI 2001 workshop on intelligent techniques for web personalization (ITWP01) (pp. 53–61).Google Scholar
  54. Newman, M. E. (2002). Assortative mixing in networks. Physical Review Letters, 89(20), 208701.CrossRefGoogle Scholar
  55. Newman, M. E. (2003). The structure and function of complex networks. SIAM Review, 45(2), 167–256.CrossRefGoogle Scholar
  56. Newman, M. (2018). Networks. Oxford: Oxford University Press.CrossRefGoogle Scholar
  57. Nguyen, A. T., Denos, N., & Berrut, C. (2007). Improving new user recommendations with rule-based induction on cold user data. In Proceedings of the 2007 ACM conference on Recommender systems (pp. 121–128). ACMGoogle Scholar
  58. Pagallo, U. (2014). Il diritto nell’età dell’informazione: il riposizionamento tecnologico degli ordinamenti giuridici tra complessità sociale, lotta per il potere e tutela dei diritti (in Italian). G. GiappichelliGoogle Scholar
  59. Page, L., Brin, S., Motwani, R., & Winograd, T. (1999). The pagerank citation ranking: Bringing order to the web. Tech. rep. Stanford InfoLabGoogle Scholar
  60. Pastor-Satorras, R., Vázquez, A., & Vespignani, A. (2001). Dynamical and correlation properties of the internet. Physical Review Letters, 87(25), 258701.CrossRefGoogle Scholar
  61. Paterek, A. (2007). Improving regularized singular value decomposition for collaborative filtering. Proceedings of KDD Cup and Workshop, 2007, 5–8.Google Scholar
  62. Peel, L., Larremore, D. B., & Clauset, A. (2017). The ground truth about metadata and community detection in networks. Science Advances, 3(5), e1602548.CrossRefGoogle Scholar
  63. Reed, D. P. (1999). That sneaky exponential–beyond metcalfe’s law to the power of community building. Context magazine, 2(1),Google Scholar
  64. Regner, T., & Riener, G. (2017). Privacy is precious: On the attempt to lift anonymity on the internet to increase revenue. Journal of Economics & Management Strategy, 26(2), 318–336.CrossRefGoogle Scholar
  65. Resnick, P., & Varian, H. R. (1997). Recommender systems. Communications of the ACM, 40(3), 56–58.CrossRefGoogle Scholar
  66. Rotundo, G., & D’Arcangelis, A. M. (2014). Network of companies: An analysis of market concentration in the italian stock market. Quality & Quantity, 48(4), 1893–1910.CrossRefGoogle Scholar
  67. 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 (pp. 285–295). ACMGoogle Scholar
  68. Scott, J., & Carrington, P. J. (2011). The SAGE handbook of social network analysis. Thousand Oaks: SAGE Publications.Google Scholar
  69. Shiller, B. R. (2014). First-degree price discrimination using big data. Tech. rep.. Brandeis Univerisity.Google Scholar
  70. Shiller, B. R. (2015). Approximating Reservation Prices From Broad Consumer Tracking, Department of Economics. Brandeis University.Google Scholar
  71. Simon, H. A. (1990). Bounded rationality. In Utility and probability (pp. 15–18). SpringerGoogle Scholar
  72. Simon, H. (1955). A behavioral model of rational choice. Quarterly Journal of Economics, 69(1), 99–118.CrossRefGoogle Scholar
  73. Swann, G. P. (2002). The functional form of network effects. Information Economics and Policy, 14(3), 417–429.CrossRefGoogle Scholar
  74. Team, R. C., et al. (2013). R: A language and environment for statistical computing. Vienna: Austria.Google Scholar
  75. The Economist (2010) Clicking for gold. how internet companies profit from data on the web. The Economist—A Special Report on Managing InformationGoogle Scholar
  76. Tsai, J. Y., Egelman, S., Cranor, L., & Acquisti, A. (2011). The effect of online privacy information on purchasing behavior: An experimental study. Information Systems Research, 22(2), 254–268.CrossRefGoogle Scholar
  77. Van Hove, L. (2016). Testing metcalfe’s law: Pitfalls and possibilities. Information Economics and Policy, 37, 67–76.CrossRefGoogle Scholar
  78. Wang, X. F., & Chen, G. (2003). Complex networks: Small-world, scale-free and beyond. IEEE Circuits and Systems Magazine, 3(1), 6–20.CrossRefGoogle Scholar
  79. Xu, R., & Wunsch, D. C. (2005). Survey of clustering algorithms. IEEE Transaction on Neural Networks, 16(3), 645–678.CrossRefGoogle Scholar
  80. Xue, G. R., Lin, C., Yang, Q., Xi, W., Zeng, H. J., Yu, Y., & Chen, Z. (2005). Scalable collaborative filtering using cluster-based smoothing. In Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval (pp. 114–121). ACMGoogle Scholar
  81. Zhang, X. Z., Liu, J. J., & Xu, Z. W. (2015). Tencent and facebook data validate metcalfe’s law. Journal of Computer Science and Technology, 30(2), 246–251.CrossRefGoogle Scholar
  82. Zhao, Q., Zhang, Y., Zhang, Y., & Friedman, D. (2016). Recommendation based on multiproduct utility maximization. Tech. rep. WZB Discussion PaperGoogle Scholar
  83. Zhou, W., Duan, W., & Piramuthu, S. (2014). A social network matrix for implicit and explicit social network plates. Decision Support Systems, 68, 89–97.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Enterprise EngineeringUniversity of Rome Tor VergataRomeItaly

Personalised recommendations