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.

Related papers

• Harm Mitigation in Recommender Systems under User Preference Dynamics [16.213153879446796]
  We consider a recommender system that takes into account the interplay between recommendations, user interests, and harmful content. We seek recommendation policies that establish a tradeoff between maximizing click-through rate (CTR) and mitigating harm.
  arXiv  Detail & Related papers  (2024-06-14T09:52:47Z)
• Measuring Strategization in Recommendation: Users Adapt Their Behavior to Shape Future Content [66.71102704873185]
  We test for user strategization by conducting a lab experiment and survey. We find strong evidence of strategization across outcome metrics, including participants' dwell time and use of "likes" Our findings suggest that platforms cannot ignore the effect of their algorithms on user behavior.
  arXiv  Detail & Related papers  (2024-05-09T07:36:08Z)
• Editable User Profiles for Controllable Text Recommendation [66.00743968792275]
  We propose LACE, a novel concept value bottleneck model for controllable text recommendations. LACE represents each user with a succinct set of human-readable concepts. It learns personalized representations of the concepts based on user documents.
  arXiv  Detail & Related papers  (2023-04-09T14:52:18Z)
• The Amplification Paradox in Recommender Systems [12.723777984461693]
  We show through simulations that the collaborative-filtering nature of recommender systems and the nicheness of extreme content can resolve the apparent paradox. Our results call for a nuanced interpretation of algorithmic amplification'' and highlight the importance of modeling the utility of content to users when auditing recommender systems.
  arXiv  Detail & Related papers  (2023-02-22T09:12:48Z)
• Eliciting User Preferences for Personalized Multi-Objective Decision Making through Comparative Feedback [76.7007545844273]
  We propose a multi-objective decision making framework that accommodates different user preferences over objectives. Our model consists of a Markov decision process with a vector-valued reward function, with each user having an unknown preference vector. We suggest an algorithm that finds a nearly optimal policy for the user using a small number of comparison queries.
  arXiv  Detail & Related papers  (2023-02-07T23:58:19Z)
• Reward Shaping for User Satisfaction in a REINFORCE Recommender [24.65853598093849]
  We propose a policy network and a satisfaction imputation network to learn which actions are satisfying to the user. The role of the imputation network is to learn which actions are satisfying to the user; while the policy network, built on top of REINFORCE, decides which items to recommend.
  arXiv  Detail & Related papers  (2022-09-30T01:29:12Z)
• Meta Policy Learning for Cold-Start Conversational Recommendation [71.13044166814186]
  We study CRS policy learning for cold-start users via meta reinforcement learning. To facilitate policy adaptation, we design three synergetic components.
  arXiv  Detail & Related papers  (2022-05-24T05:06:52Z)
• Explicit User Manipulation in Reinforcement Learning Based Recommender Systems [0.0]
  Reinforcement learning based recommender systems can learn to influence users if that means maximizing clicks, engagement, or consumption. Social media has been shown to be a contributing factor to increased political polarization. explicit user manipulation in which the beliefs and opinions of users are tailored towards a certain end emerges as a significant concern.
  arXiv  Detail & Related papers  (2022-03-20T19:03:18Z)
• Learning to Learn a Cold-start Sequential Recommender [70.5692886883067]
  The cold-start recommendation is an urgent problem in contemporary online applications. We propose a meta-learning based cold-start sequential recommendation framework called metaCSR. metaCSR holds the ability to learn the common patterns from regular users' behaviors.
  arXiv  Detail & Related papers  (2021-10-18T08:11:24Z)
• PURS: Personalized Unexpected Recommender System for Improving User Satisfaction [76.98616102965023]
  We describe a novel Personalized Unexpected Recommender System (PURS) model that incorporates unexpectedness into the recommendation process. Extensive offline experiments on three real-world datasets illustrate that the proposed PURS model significantly outperforms the state-of-the-art baseline approaches.
  arXiv  Detail & Related papers  (2021-06-05T01:33:21Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.