Recommending to Strategic Users

• URL: http://arxiv.org/abs/2302.06559v1
• Date: Mon, 13 Feb 2023 17:57:30 GMT
• Title: Recommending to Strategic Users
• Authors: Andreas Haupt, Dylan Hadfield-Menell and Chara Podimata
• Abstract summary: We show that users strategically choose content to influence the types of content they get recommended in the future. We propose three interventions that may improve recommendation quality when taking into account strategic consumption.
• Score: 10.079698681921673
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Recommendation systems are pervasive in the digital economy. An important assumption in many deployed systems is that user consumption reflects user preferences in a static sense: users consume the content they like with no other considerations in mind. However, as we document in a large-scale online survey, users do choose content strategically to influence the types of content they get recommended in the future. We model this user behavior as a two-stage noisy signalling game between the recommendation system and users: the recommendation system initially commits to a recommendation policy, presents content to the users during a cold start phase which the users choose to strategically consume in order to affect the types of content they will be recommended in a recommendation phase. We show that in equilibrium, users engage in behaviors that accentuate their differences to users of different preference profiles. In addition, (statistical) minorities out of fear of losing their minority content exposition may not consume content that is liked by mainstream users. We next propose three interventions that may improve recommendation quality (both on average and for minorities) when taking into account strategic consumption: (1) Adopting a recommendation system policy that uses preferences from a prior, (2) Communicating to users that universally liked ("mainstream") content will not be used as basis of recommendation, and (3) Serving content that is personalized-enough yet expected to be liked in the beginning. Finally, we describe a methodology to inform applied theory modeling with survey results.

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