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.

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