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

• URL: http://arxiv.org/abs/2309.00940v1
• Date: Sat, 2 Sep 2023 13:35:11 GMT
• Title: Content Prompting: Modeling Content Provider Dynamics to Improve User Welfare in Recommender Ecosystems
• Authors: Siddharth Prasad, Martin Mladenov, Craig Boutilier
• Abstract summary: 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. We aim to determine a joint prompting policy that induces a set of providers to make content available that optimize user social welfare in equilibrium.
• Score: 14.416231654089994
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• Unveiling User Satisfaction and Creator Productivity Trade-Offs in Recommendation Platforms [68.51708490104687]
  We show that a purely relevance-driven policy with low exploration strength boosts short-term user satisfaction but undermines the long-term richness of the content pool. Our findings reveal a fundamental trade-off between immediate user satisfaction and overall content production on platforms.
  arXiv  Detail & Related papers  (2024-10-31T07:19:22Z)
• EmbSum: Leveraging the Summarization Capabilities of Large Language Models for Content-Based Recommendations [38.44534579040017]
  We introduce EmbSum, a framework that enables offline pre-computations of users and candidate items. The model's ability to generate summaries of user interests serves as a valuable by-product, enhancing its usefulness for personalized content recommendations.
  arXiv  Detail & Related papers  (2024-05-19T04:31:54Z)
• User Welfare Optimization in Recommender Systems with Competing Content Creators [65.25721571688369]
  In this study, we perform system-side user welfare optimization under a competitive game setting among content creators. We propose an algorithmic solution for the platform, which dynamically computes a sequence of weights for each user based on their satisfaction of the recommended content. These weights are then utilized to design mechanisms that adjust the recommendation policy or the post-recommendation rewards, thereby influencing creators' content production strategies.
  arXiv  Detail & Related papers  (2024-04-28T21:09:52Z)
• 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)
• Recommending to Strategic Users [10.079698681921673]
  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.
  arXiv  Detail & Related papers  (2023-02-13T17:57:30Z)
• Explainability in Music Recommender Systems [69.0506502017444]
  We discuss how explainability can be addressed in the context of Music Recommender Systems (MRSs) MRSs are often quite complex and optimized for recommendation accuracy. We show how explainability components can be integrated within a MRS and in what form explanations can be provided.
  arXiv  Detail & Related papers  (2022-01-25T18:32:11Z)
• FEBR: Expert-Based Recommendation Framework for beneficial and personalized content [77.86290991564829]
  We propose FEBR (Expert-Based Recommendation Framework), an apprenticeship learning framework to assess the quality of the recommended content. The framework exploits the demonstrated trajectories of an expert (assumed to be reliable) in a recommendation evaluation environment, to recover an unknown utility function. We evaluate the performance of our solution through a user interest simulation environment (using RecSim)
  arXiv  Detail & Related papers  (2021-07-17T18:21:31Z)
• Towards Content Provider Aware Recommender Systems: A Simulation Study on the Interplay between User and Provider Utilities [34.288256311920904]
  We build a REINFORCE recommender agent, coined EcoAgent, to optimize a joint objective of user utility and the counterfactual utility lift of the provider associated with the recommended content. We offer a number of simulated experiments that shed light on both the benefits and the limitations of our approach.
  arXiv  Detail & Related papers  (2021-05-06T00:02:58Z)
• Optimizing Long-term Social Welfare in Recommender Systems: A Constrained Matching Approach [36.54379845220444]
  We study settings in which content providers cannot remain viable unless they receive a certain level of user engagement. Our model ensures the system reaches an equilibrium with maximal social welfare supported by a sufficiently diverse set of viable providers. We draw connections to various notions of user regret and fairness, arguing that these outcomes are fairer in a utilitarian sense.
  arXiv  Detail & Related papers  (2020-07-31T22:40:47Z)
• Sequential Recommender Systems: Challenges, Progress and Prospects [50.12218578518894]
  sequential recommender systems (SRSs) try to understand and model the sequential user behaviors, the interactions between users and items, and the evolution of users preferences and item popularity over time. We first present the characteristics of SRSs, then summarize and categorize the key challenges in this research area, followed by the corresponding research progress consisting of the most recent and representative developments on this topic.
  arXiv  Detail & Related papers  (2019-12-28T05:12:28Z)

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.