  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.

Machine Learning;Information Retrieval