The limits of popularity-based recommendations, and the role of social ties

Marco Bressan; Stefano Leucci; Alessandro Panconesi; Prabhakar Raghavan; Erisa Terolli

doi:10.1145/2939672.2939797

The limits of popularity-based recommendations, and the role of social ties

Marco Bressan, Stefano Leucci, Alessandro Panconesi, Prabhakar Raghavan, Erisa Terolli

Department of Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

18 Scopus citations

Abstract

In this paper we introduce a mathematical model that captures some of the salient features of recommender systems that are based on popularity and that try to exploit social ties among the users. We show that, under very general conditions, the market always converges to a steady state, for which we are able to give an explicit form. Thanks to this we can tell rather precisely how much a market is altered by a recommendation system, and determine the power of users to influence others. Our theoretical results are complemented by experiments with real world social networks showing that social graphs prevent large market distortions in spite of the presence of highly influential users.

Original language	English
Title of host publication	KDD 2016 - Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
Pages	745-754
Number of pages	10
ISBN (Electronic)	9781450342322
DOIs	https://doi.org/10.1145/2939672.2939797
State	Published - 13 Aug 2016
Event	22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2016 - San Francisco, United States Duration: 13 Aug 2016 → 17 Aug 2016

Publication series

Name	Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
Volume	13-17-August-2016

Conference

Conference	22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2016
Country/Territory	United States
City	San Francisco
Period	13/08/16 → 17/08/16

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10.1145/2939672.2939797

Cite this

Bressan, M., Leucci, S., Panconesi, A., Raghavan, P., & Terolli, E. (2016). The limits of popularity-based recommendations, and the role of social ties. In KDD 2016 - Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 745-754). (Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining; Vol. 13-17-August-2016). https://doi.org/10.1145/2939672.2939797

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title = "The limits of popularity-based recommendations, and the role of social ties",

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author = "Marco Bressan and Stefano Leucci and Alessandro Panconesi and Prabhakar Raghavan and Erisa Terolli",

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Bressan, M, Leucci, S, Panconesi, A, Raghavan, P & Terolli, E 2016, The limits of popularity-based recommendations, and the role of social ties. in KDD 2016 - Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, vol. 13-17-August-2016, pp. 745-754, 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2016, San Francisco, United States, 13/08/16. https://doi.org/10.1145/2939672.2939797

The limits of popularity-based recommendations, and the role of social ties. / Bressan, Marco; Leucci, Stefano; Panconesi, Alessandro et al.
KDD 2016 - Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2016. p. 745-754 (Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining; Vol. 13-17-August-2016).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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Bressan M, Leucci S, Panconesi A, Raghavan P, Terolli E. The limits of popularity-based recommendations, and the role of social ties. In KDD 2016 - Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2016. p. 745-754. (Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining). doi: 10.1145/2939672.2939797

The limits of popularity-based recommendations, and the role of social ties

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