Content Prompting: Modeling Content Provider Dynamics to Improve User Welfare in Recommender Ecosystems

TL;DR

This paper introduces content prompting policies to address information asymmetry in recommender systems, aiming to enhance user welfare by encouraging providers to offer content that meets unmet demand.

Contribution

It presents a comprehensive model of the recommender ecosystem, designs sequential prompting policies, formulates an optimization approach, and demonstrates potential benefits through experiments.

Findings

Prompting policies can improve ecosystem health.

Content prompts lead to increased user satisfaction.

Optimization of prompts enhances overall user welfare.

Abstract

Users derive value from a recommender system (RS) only to the extent that it is able to surface content (or items) that meet their needs/preferences. While RSs often have a comprehensive view of user preferences across the entire user base, content providers, by contrast, generally have only a local view of the preferences of users that have interacted with their content. This limits a provider's ability to offer new content to best serve the broader population. In this work, we tackle this information asymmetry with content prompting policies. A content prompt is a hint or suggestion to a provider to make available novel content for which the RS predicts unmet user demand. A prompting policy is a sequence of such prompts that is responsive to the dynamics of a provider's beliefs, skills and incentives. We aim to determine a joint prompting policy that induces a set of providers to make content available that optimizes user social welfare in equilibrium, while respecting the incentives of the providers themselves. Our contributions include: (i) an abstract model of the RS ecosystem, including content provider behaviors, that supports such prompting; (ii) the design and theoretical analysis of sequential prompting policies for individual providers; (iii) a mixed integer programming formulation for optimal joint prompting using path planning in content space; and (iv) simple, proof-of-concept experiments illustrating how such policies improve ecosystem health and user welfare.