Seminar: The Structure of Social Influence in Recommender Networks

Seminar: The Structure of Social Influence in Recommender Networks

  • Datum: 22.09.2020
  • Uhrzeit: 15:30
  • Vortragender: Pantelis Pipergias Analytis
  • Ort: online
  • Gastgeber: Center for Humans and Machines
  • Kontakt: sekrahwan@mpib-berlin.mpg.de

Pantelis Pipergias Analytis, University of Southern Denmark

The Structure of Social Influence in Recommender Networks

People’s ability to influence others’ opinion on matters of taste varies greatly—both offline and in recommender systems. What are the mechanisms underlying these striking differences? Using the weighted k-nearest neighbors algorithm (k-nn) to represent an array of social learning strategies, Pantelis Pipergias Analytis and his team show—leveraging methods from network science—how the k-nn algorithm gives rise to networks of social influence in six real-world domains of taste. The scientists show three novel results that apply both to offline advice taking and online recommender settings. First, influential individuals have mainstream tastes and high dispersion in their taste similarity with others. Second, the fewer people an individual or algorithm consults (i.e., the lower k is) or the larger the weight placed on the opin- ions of more similar others, the smaller the group of people with substantial influence. Third, the influence networks emerging from deploying the k-nn algorithm are hierarchically organized. Their results shed new light on classic empirical findings in communication and network science and can help improve the understanding of social influence offline and online.

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