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Most existing recommender systems focus primarily on matching users tocontent which maximizes user satisfaction on the platform. It is increasinglyobvious, however, that content providers have a critical influence on usersatisfaction through content creation, largely determining the content poolavailable for recommendation. A natural question thus arises: can we designrecommenders taking into account the long-term utility of both users andcontent providers? By doing so, we hope to sustain more providers and a morediverse content pool for long-term user satisfaction. Understanding the fullimpact of recommendations on both user and provider groups is challenging. Thispaper aims to serve as a research investigation of one approach toward buildinga provider-aware recommender, and evaluating its impact in a simulated setup.  To characterize the user-recommender-provider interdependence, we complementuser modeling by formalizing provider dynamics as well. The resulting jointdynamical system gives rise to a weakly-coupled partially observable Markovdecision process driven by recommender actions and user feedback to providers.We then build a REINFORCE recommender agent, coined EcoAgent, to optimize ajoint objective of user utility and the counterfactual utility lift of theprovider associated with the recommended content, which we show to beequivalent to maximizing overall user utility and the utilities of allproviders on the platform under some mild assumptions. To evaluate ourapproach, we introduce a simulation environment capturing the key interactionsamong users, providers, and the recommender. We offer a number of simulatedexperiments that shed light on both the benefits and the limitations of ourapproach. These results help understand how and when a provider-awarerecommender agent is of benefit in building multi-stakeholder recommendersystems.

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