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Simulating the Impact of Recommender Systems on the Evolution of Collective Users' Choices

Published:13 July 2020Publication History

ABSTRACT

The major focus of recommender systems (RSs) research is on improving the goodness of the generated recommendations. Less attention has been dedicated to understand the effect of an RS on the actual users' choices. Hence, in this paper, we propose a novel simulation model of users' choices under the influence of an RS. The model leverages real rating/choice data observed up to a point in time in order to simulate next, month-by-month, choices of the users. We have analysed choice diversity, popularity and utility and found that: RSs have different effects on the users' choices; the behaviour of new users is particularly important to understand collective choices; and the users' previous knowledge, i.e., their "awareness" of the item catalogue greatly affects choice diversity.

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      cover image ACM Conferences
      HT '20: Proceedings of the 31st ACM Conference on Hypertext and Social Media
      July 2020
      327 pages
      ISBN:9781450370981
      DOI:10.1145/3372923

      Copyright © 2020 ACM

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      Publication History

      • Published: 13 July 2020

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