Solutions to preference manipulation in recommender systems require knowledge of meta-preferences

• URL: http://arxiv.org/abs/2209.11801v1
• Date: Wed, 14 Sep 2022 15:01:13 GMT
• Title: Solutions to preference manipulation in recommender systems require knowledge of meta-preferences
• Authors: Hal Ashton, Matija Franklin
• Abstract summary: Some preference changes on the part of the user are self-induced and desired whether the recommender caused them or not. This paper proposes that solutions to preference manipulation in recommender systems must take into account certain meta-preferences.
• Score: 7.310043452300736
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Iterative machine learning algorithms used to power recommender systems often change people's preferences by trying to learn them. Further a recommender can better predict what a user will do by making its users more predictable. Some preference changes on the part of the user are self-induced and desired whether the recommender caused them or not. This paper proposes that solutions to preference manipulation in recommender systems must take into account certain meta-preferences (preferences over another preference) in order to respect the autonomy of the user and not be manipulative.

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