The closed loop between opinion formation and personalised recommendations

Wilbert Samuel Rossi, Jan Willem Polderman, Paolo Frasca

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

In online platforms, recommender systems are responsible for directing users to relevant content. In order to enhance the users engagement, recommender systems adapt their output to the reactions of the users, who are in turn affected by the recommended content. In this work, we study a tractable analytical model of a user that interacts with an online news aggregator, with the purpose of making explicit the feedback loop between the evolution of the users opinion and the personalised recommendation of content. More specifically, we assume that the user is endowed with a scalar opinion about a certain issue and seeks news about it on a news aggregator: this opinion is influenced by all received news, which are characterized by a binary position on the issue at hand. The user is affected by a confirmation bias, that is, a preference for news that confirm her current opinion. The news aggregator recommends items with the goal of maximizing the number of users clicks (as a measure of her engagement): in order to fulfil its goal, the recommender has to compromise between exploring the users preferences and exploiting what it has learned so far. After defining sui

Original languageEnglish
JournalIEEE Transactions on Control of Network Systems
DOIs
Publication statusAccepted/In press - 2021

Keywords

  • Analytical models
  • Art
  • Control systems
  • Data models
  • Mathematical model
  • Numerical models
  • Recommender systems

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