article

From blurry numbers to clear preferences: A mechanism to extract reputation in social networks

Authors:

Ramón Hermoso,

Roberto Centeno,

Maria FasliAuthors Info & Claims

Expert Systems with Applications: An International Journal, Volume 41, Issue 5

Pages 2269 - 2285

https://doi.org/10.1016/j.eswa.2013.09.025

Published: 25 November 2019 Publication History

Abstract

Complex social networks are typically used in order to represent and structure social relationships that do not follow a predictable pattern of behaviour. Due to their openness and dynamics, these networks make participants continuously deal with uncertainty before any type of interaction. Reputation appears as a key concept helping users to mitigate such uncertainty. Most of the reputation mechanisms proposed in the literature are based on numerical opinions (ratings), and consequently, they are exposed to potential problems such as the subjectivity in the opinions and their consequent inaccurate aggregation. With these problems in mind, this paper presents a reputation mechanism based on the concepts of pairwise elicitation processes and knock-out tournaments. The main objective of this mechanism is to build reputation rankings from qualitative opinions, thereby removing the subjectivity problems associated with the aggregation of quantitative opinions. The proposed approach is evaluated with different data sets from the MovieLens and Flixster web sites.

References

[1]

Altman, A., & Tennenholtz, M. (2005). Ranking systems: The pagerank axioms. In: Proceedings of the 6th ACM conference on Electronic commerce, EC '05 (pp. 1-8). New York, NY, USA: ACM.

Digital Library

[2]

Bachrach, Y., Graepel, T., Kasneci, G., Kosinski, M., & Van Gael, J. (2012). Crowd iq: Aggregating opinions to boost performance. In: Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems - Volume 1. AAMAS '12. International Foundation for Autonomous Agents and Multiagent Systems (pp. 535-542). Richland, SC.

Digital Library

[3]

Balakrishnan, S., & Chopra, S. (2010). Two of a kind or the ratings game? adaptive pairwise preferences and latent factor models. In: Proceedings of the 2010 IEEE international conference on data mining. ICDM '10. IEEE Computer Society (pp. 725-730). Washington, DC, USA.

Digital Library

[4]

How effective are electronic reputation mechanisms? An experimental investigation. Management Science. v50 i11. 1587-1602.

Digital Library

[5]

Voting procedures. In: Arrow, K.J., Sen, A.K., Suzumura, K. (Eds.), Handbook of social choice and welfare, Vol. 1. Elsevier. pp. 173-236.

[6]

Burke, R. (2000). Knowledge-based recommender systems. In Encyclopedia of library and information systems (Vol. 69).

[7]

Cambria, E., Livingstone, A., & Hussain, A. (2012). The hourglass of emotions. In Cognitive behavioural systems lecture notes in computer science (Vol. 74, pp. 144-157). Springer.

Digital Library

[8]

Cambria, E., Havasi, C., & Hussain, A. (2012). SenticNet 2: A semantic and affective resource for opinion mining and sentiment analysis. In FLAIRS (pp. 202-207). Marco Island.

[9]

Sentic Computing: Techniques tools and applications. Springer briefs in cognitive computation, 2012.Springer.

Digital Library

[10]

New avenues in opinion mining and sentiment analysis. IEEE Intelligent Systems. v28 i2. 15-21.

Digital Library

[11]

Chen, L., & Pu, P. (2004). Survey of preference elicitation methods. Tech. rep., Swiss Federal Institure of Technology in Lausanne (EPFL).

[12]

Conitzer, V. (2007). Eliciting single-peaked preferences using comparison queries. In Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems, AAMAS '07 (pp. 651-658). New York, NY, USA: ACM.

Digital Library

[13]

Conitzer, V., Rognlie, M., & Xia, L. (2009). Preference functions that score rankings and maximum likelihood estimation. In Proceedings of the 21st international joint conference on Artifical intelligence, IJCAI'09 (pp. 109-115). San Francisco, CA, USA: Morgan Kaufmann Publishers Inc.

Digital Library

[14]

Dalvi, N., Kumar, R., & Pang, B. (2013). Para 'normal' activity: On the distribution of average ratings. In Proceedings of 7th international AAAI conference on weblogs and social media (pp. 110-119).

[15]

David, S., & Pinch, T. J., (2005). Six degrees of reputation: The use and abuse of online review and recommendation systems. Social Science Research Network Working Paper Series.

[16]

Dellarocas, C. (2000). Mechanisms for coping with unfair ratings and discriminatory behavior in online reputation reporting systems. In ICIS '00: Proceedings of the twenty first international conference on Information systems. Association for Information Systems (pp. 520-525). Atlanta, GA, USA.

Digital Library

[17]

Reputation-based partner choice promotes cooperation in social networks. Phys. Rev. E. v78. 026117

[18]

Fullam, K. K., & Barber, K. S. (2007). Dynamically learning sources of trust information: Experience vs. reputation. In Proceedings of AAMAS '07 (pp. 1055-1062). New York, NY, USA: ACM.

Digital Library

[19]

Manipulation of voting schemes: A general result. Econometrica. v41 i4. 587-601.

[20]

An integrated trust and reputation model for open multi-agent systems. Autonomous Agents and Multi-Agent Systems. v13 i2. 119-154.

Digital Library

[21]

Cumulated gain-based evaluation of IR techniques. ACM Transactions on Information Systems. v20 i4. 422-446.

Digital Library

[22]

Jøsang, A., & Ismail, R. (2002). The beta reputation system. In Proceedings of the 15th Bled electronic commerce conference.

[23]

A survey of trust and reputation systems for online service provision. Decision Support Systems. v43 i2. 618-644.

Digital Library

[24]

Jurca, R., & Faltings, B. (2008). Incentives for expressing opinions in online polls. In Proceedings of the 9th ACM conference on Electronic commerce. EC '08 (pp. 119-128). New York, NY, USA: ACM.

Digital Library

[25]

The Mario AI benchmark and competitions. IEEE Transactions Computational Intelligence and AI in Games. v4 i1. 55-67.

[26]

CRM: An efficient trust and reputation model for agent computing. Knowledge-Based Systems. v30 i0. 1-16.

Digital Library

[27]

A novel reputation computation model based on subjective logic for mobile ad hoc networks. Future Generation Computer Systems. v27 i5. 547-554.

Digital Library

[28]

Negahban, S., Oh, S., & Shah, D. (2012). Iterative ranking from pair-wise comparisons. In Proceedings of the 26th annual conference on neural information processing systems (pp. 2483-2491).

[29]

Networks: An introduction. Oxford University Press.

[30]

Computational trust and reputation models for open multi-agent systems: A review. Artificial Intelligence Review. v40 i1. 1-25.

Digital Library

[31]

Voting in multi-agent systems. Computer Journal. v49 i2. 156-170.

Digital Library

[32]

Pujol, J. M., Sangüesa, R., & Delgado, J. (2002). Extracting reputation in multi agent systems by means of social network topology. In Proceedings of the first international joint conference on Autonomous agents and multiagent systems: part 1. AAMAS '02 (pp. 467-474). New York, NY, USA: ACM.

Digital Library

[33]

Trust in multi-agent systems. Knowledge Engineering Review. v19 i1. 1-25.

[34]

Reputation and efficiency in social interactions: An example of network effects. American Journal of Sociology. v96. 626-654.

[35]

Reputation systems. Communications of the ACM. v43 i12. 45-48.

[36]

Shearin, S., & Lieberman, H. (2001). Intelligent profiling by example. In Proceedings of the 6th international conference on Intelligent user interfaces, IUI '01 (pp. 145-151). New York, NY, USA: ACM.

Digital Library

[37]

Review on computational trust and reputation models. Artificial Intelligence Review. v24 i1. 33-60.

Digital Library

[38]

An efficient and versatile approach to trust and reputation using hierarchical bayesian modelling. Artificial Intelligence. v193. 149-185.

Digital Library

[39]

Travos: Trust and reputation in the context of inaccurate information sources. Autonomous Agents and Multi-Agent Systems. v12 i2. 183-198.

Digital Library

[40]

Complex Social Networks. Cambridge University Press.

[41]

Vu, T., & Shoham, Y. (2011). Fair seeding in knockout tournaments. ACM Transactions on Intelligent Systems and Technology 3(1), 9:1-9:17.

Digital Library

[42]

Vu, T., Altman, A., & Shoham, Y. (2009). On the complexity of schedule control problems for knockout tournaments. In Proceedings of AAMAS'09. Vol. 1 of AAMAS '09 (pp. 225-232). Richland, SC: IFAAMAS.

Digital Library

[43]

Yu, B., & Singh, M. P. (2002). An evidential model of distributed reputation management. In AAMAS '02: Proceedings of the first international joint conference on Autonomous agents and multiagent systems (pp. 294-301). New York, NY, USA: ACM Press.

Digital Library

[44]

Yu, B., Singh, M. P., & Sycara, K. (2004). Developing trust in large-scale peer-to-peer systems. In: Proceedings of first IEEE symposium on multi-agent security and survivability (pp. 1-10).

Cited By

Joaquín FTamargo LGarcía A(2022)Formalization and implementation of credibility dynamics through prioritized multiple revisionInternational Journal of Approximate Reasoning10.1016/j.ijar.2022.05.001147:C(1-22)Online publication date: 1-Aug-2022
https://dl.acm.org/doi/10.1016/j.ijar.2022.05.001
Ghiasi HFathian Brojeny MGholamian M(2018)A reputation system for e-marketplaces based on pairwise comparisonKnowledge and Information Systems10.1007/s10115-017-1141-256:3(613-636)Online publication date: 1-Sep-2018
https://dl.acm.org/doi/10.1007/s10115-017-1141-2
Centeno RHermoso R(2018)Estimating global opinions by keeping users from fraud in online review systemsKnowledge and Information Systems10.1007/s10115-017-1089-255:2(467-491)Online publication date: 1-May-2018
https://dl.acm.org/doi/10.1007/s10115-017-1089-2
Show More Cited By

Recommendations

On the inaccuracy of numerical ratings: dealing with biased opinions in social networks

In this work, we study the potential problems emanating from using numerical ratings in social networks to rank entities regarding their reputation. In particular, we empirically demonstrate how reputation rankings as collected and managed by current ...
Determinants of repurchase intention in online group-buying

A theoretical model integrating the D&M model and trust literature was proposed.The model was tested using data collected customers of a group-buying website.Increasing satisfaction and perceived quality will facilitate repurchase intention.Increasing ...
Paying It Forward vs. Rewarding Reputation: Mechanisms of Generalized Reciprocity

Generalized reciprocity is a widely recognized but little studied component of social capital in organizations. We develop a causal model of the multiple mechanisms that sustain generalized reciprocity in an organization, drawing together disparate ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image Expert Systems with Applications: An International Journal

Expert Systems with Applications: An International Journal Volume 41, Issue 5

April, 2014

509 pages

ISSN:0957-4174

Issue’s Table of Contents

Copyright © Elsevier Ltd © 2013.

Publisher

Pergamon Press, Inc.

United States

Publication History

Published: 25 November 2019

Author Tags

Qualifiers

Article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

7
Total Citations
View Citations
0
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Reflects downloads up to 28 Nov 2024

Other Metrics

View Author Metrics

Citations

Cited By

Joaquín FTamargo LGarcía A(2022)Formalization and implementation of credibility dynamics through prioritized multiple revisionInternational Journal of Approximate Reasoning10.1016/j.ijar.2022.05.001147:C(1-22)Online publication date: 1-Aug-2022
https://dl.acm.org/doi/10.1016/j.ijar.2022.05.001
Ghiasi HFathian Brojeny MGholamian M(2018)A reputation system for e-marketplaces based on pairwise comparisonKnowledge and Information Systems10.1007/s10115-017-1141-256:3(613-636)Online publication date: 1-Sep-2018
https://dl.acm.org/doi/10.1007/s10115-017-1141-2
Centeno RHermoso R(2018)Estimating global opinions by keeping users from fraud in online review systemsKnowledge and Information Systems10.1007/s10115-017-1089-255:2(467-491)Online publication date: 1-May-2018
https://dl.acm.org/doi/10.1007/s10115-017-1089-2
Aravazhi Irissappane AZhang J(2017)Filtering unfair ratings from dishonest advisors in multi-criteria e-marketsAutonomous Agents and Multi-Agent Systems10.1007/s10458-015-9314-431:1(36-65)Online publication date: 1-Jan-2017
https://dl.acm.org/doi/10.1007/s10458-015-9314-4
Centeno RHermoso RWeiss GYolum PBordini RElkind E(2015)When Opinion Request Meets Majority SearchProceedings of the 2015 International Conference on Autonomous Agents and Multiagent Systems10.5555/2772879.2773446(1805-1806)Online publication date: 4-May-2015
https://dl.acm.org/doi/10.5555/2772879.2773446
Coronado MIglesias CSerrano E(2015)Modelling rules for automating the Evented WEb by semantic technologiesExpert Systems with Applications: An International Journal10.1016/j.eswa.2015.06.03142:21(7979-7990)Online publication date: 30-Nov-2015
https://dl.acm.org/doi/10.1016/j.eswa.2015.06.031
Centeno RHermoso RFasli M(2015)On the inaccuracy of numerical ratingsInformation Systems Frontiers10.1007/s10796-014-9526-117:4(809-825)Online publication date: 1-Aug-2015
https://dl.acm.org/doi/10.1007/s10796-014-9526-1

View Options

View options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

Media

Figures

Other

Tables

View Issue’s Table of Contents