The Structure of Social Influence in Recommender Networks

Pantelis Pipergias Analytis, Daniel Barkoczi, Philipp Lorenz-Spreen, Stefan M. Herzog

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review


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, we 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. We 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 opinions 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. Our 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.
Original languageEnglish
Title of host publicationProceedings of The Web Conference 2020
PublisherAssociation for Computing Machinery
Publication date20. Apr 2020
ISBN (Electronic)9781450370233
Publication statusPublished - 20. Apr 2020
EventThe Web Conference 2020 - Taipei, Taiwan, Province of China
Duration: 20. Apr 202024. Apr 2020


ConferenceThe Web Conference 2020
Country/TerritoryTaiwan, Province of China


  • collaborative filtering
  • influencers
  • social influence
  • social networks


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