Towards Content Provider Aware Recommender Systems: A Simulation Study on the Interplay between User and Provider Utilities

• URL: http://arxiv.org/abs/2105.02377v1
• Date: Thu, 6 May 2021 00:02:58 GMT
• Title: Towards Content Provider Aware Recommender Systems: A Simulation Study on the Interplay between User and Provider Utilities
• Authors: Ruohan Zhan, Konstantina Christakopoulou, Ya Le, Jayden Ooi, Martin Mladenov, Alex Beutel, Craig Boutilier, Ed H. Chi, Minmin Chen
• Abstract summary: 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.
• Score: 34.288256311920904
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Most existing recommender systems focus primarily on matching users to content which maximizes user satisfaction on the platform. It is increasingly obvious, however, that content providers have a critical influence on user satisfaction through content creation, largely determining the content pool available for recommendation. A natural question thus arises: can we design recommenders taking into account the long-term utility of both users and content providers? By doing so, we hope to sustain more providers and a more diverse content pool for long-term user satisfaction. Understanding the full impact of recommendations on both user and provider groups is challenging. This paper aims to serve as a research investigation of one approach toward building a provider-aware recommender, and evaluating its impact in a simulated setup. To characterize the user-recommender-provider interdependence, we complement user modeling by formalizing provider dynamics as well. The resulting joint dynamical system gives rise to a weakly-coupled partially observable Markov decision process driven by recommender actions and user feedback to providers. We then 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, which we show to be equivalent to maximizing overall user utility and the utilities of all providers on the platform under some mild assumptions. To evaluate our approach, we introduce a simulation environment capturing the key interactions among users, providers, and the recommender. We offer a number of simulated experiments that shed light on both the benefits and the limitations of our approach. These results help understand how and when a provider-aware recommender agent is of benefit in building multi-stakeholder recommender systems.

Related papers

• Content Prompting: Modeling Content Provider Dynamics to Improve User Welfare in Recommender Ecosystems [14.416231654089994]
  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.
  arXiv  Detail & Related papers  (2023-09-02T13:35:11Z)
• User-Controllable Recommendation via Counterfactual Retrospective and Prospective Explanations [96.45414741693119]
  We present a user-controllable recommender system that seamlessly integrates explainability and controllability. By providing both retrospective and prospective explanations through counterfactual reasoning, users can customize their control over the system.
  arXiv  Detail & Related papers  (2023-08-02T01:13:36Z)
• 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)
• Influential Recommender System [12.765277278599541]
  We present Influential Recommender System (IRS), a new recommendation paradigm that aims to proactively lead a user to like a given objective item. IRS progressively recommends to the user a sequence of carefully selected items (called an influence path) We show that IRN significantly outperforms the baseline recommenders and demonstrates its capability of influencing users' interests.
  arXiv  Detail & Related papers  (2022-11-18T03:04:45Z)
• 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)
• 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)
• 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)
• PinnerSage: Multi-Modal User Embedding Framework for Recommendations at Pinterest [54.56236567783225]
  PinnerSage is an end-to-end recommender system that represents each user via multi-modal embeddings. We conduct several offline and online A/B experiments to show that our method significantly outperforms single embedding methods.
  arXiv  Detail & Related papers  (2020-07-07T17:13:20Z)

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.