Mechanism Design for Personalized Recommender Systems

Qingpeng Cai, Aris Filos-Ratsikas, Chang Liu, Pingzhong Tang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract / Description of output

Strategic behaviour from sellers on e-commerce websites, such as faking transactions and manipulating the recommendation scores through artificial reviews, have been among the most notorious obstacles that prevent websites from maximizing the efficiency of their recommendations. Previous approaches have focused almost exclusively on machine learning-related techniques to detect and penalize such behaviour. In this paper, we tackle the problem from a different perspective, using the approach of the field of mechanism design. We put forward a game model tailored for the setting at hand and aim to construct truthful mechanisms, i.e. mechanisms that do not provide incentives for dishonest reputation-augmenting actions, that guarantee good recommendations in the worst-case. For the setting with two agents, we propose a truthful mechanism that is optimal in terms of social efficiency. For the general case of m agents, we prove both lower and upper bound results on the effciency of truthful mechanisms and propose truthful mechanisms that yield significantly better results, when compared to an existing mechanism from a leading e-commerce site on real data.
Original languageEnglish
Title of host publicationProceedings of the 10th ACM Conference on Recommender Systems
Place of PublicationNew York, NY, USA
PublisherAssociation for Computing Machinery, Inc
Number of pages8
ISBN (Print)9781450340359
Publication statusPublished - 7 Sept 2016
EventThe 10th ACM Conference on Recommender Systems, 2016 - Boston, United States
Duration: 15 Sept 201619 Sept 2016
Conference number: 10

Publication series

NameRecSys '16
PublisherAssociation for Computing Machinery


ConferenceThe 10th ACM Conference on Recommender Systems, 2016
Abbreviated titleRecSys 2016
Country/TerritoryUnited States
Internet address

Keywords / Materials (for Non-textual outputs)

  • mechanism design
  • approximation
  • reputation systems


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