# Content Provider Dynamics and Coordination in Recommendation Ecosystems

NeurIPS, 2020.

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Abstract:

Recommendation Systems like YouTube are vibrant ecosystems with two types of users: Content consumers (those who watch videos) and content providers (those who create videos). While the computational task of recommending relevant content is largely solved, designing a system that guarantees high social welfare for all stakeholders is stil...More

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Introduction

- Recommendation systems (RSs hereinafter) play a major role in the life nowadays. Many modern RSs, like YouTube, Medium, or Spotify, recommend content created by others and go far beyond recommendations.
- These fluctuations change the utility of the providers and the social welfare of the consumers, defined as the quality of their proposed content.
- Given the demand for topic k, a strategy profile a, and the quality Q of the blogs for the selected topics in a, the recommendation function R recommends content, possibly in a randomized manner.

Highlights

- Recommendation systems (RSs hereinafter) play a major role in our life nowadays
- Successful content providers rely on the RS for some part of their income: Advertising, affiliated marketing, sponsorship, and merchandise; unsatisfied content providers might decide to provide a different type of content or even abandon the RS
- We show that the provider dynamics always converges to a stable point, but the convergence time may be long
- We suggest that a “you wrote this, would you be interested in writing on that?” feature could be revolutionary as well—contributing to better social welfare of content consumers, as well as the utility of content providers. Such a policy could be implemented in practice by a direct recommendation to providers, or by a more moderate action like nudging content providers to experiment with a different set of contents
- To support our vision of content provider coordination in RSs even further, we show in the appendix that the ratio between the social welfare of the best equilibrium and the worst equilibrium is unbounded
- Such a coordination between content providers may lead to a significant lift in social welfare

Results

- The authors represent a game as a tuple P, T , D, Q, C, R, U , where P is the authors, T is the topics, D is the demand for topics, Q and C are the quality and conversion matrices, R is the recommendation function, and U is the utility function.
- Let Hk(a) denote the set of authors whose documents have the highest quality among those who write on topic k under
- For the purpose of social welfare maximization, it suffices to consider candidate profiles in which every topic is selected by at most one author.
- If the authors create several copies of the same topic, high-quality players would block low-quality authors matched to it.
- Tis the set of unmatched topics; Lk is a lower bound on the load on topic k, namely the ongoing number of players the authors matched to it; X, Y and E are the elements of the bipartite graph G (Y stores the set of unmatched players); and a∗ is a non-valid, empty profile that the authors construct as the algorithm advances.
- The authors first find the set of highest-quality players for every topic k, denoted Ak (Line 7).
- For every topic k, the authors consider the set of most profitable players w.r.t. k and their potential utility if matched to k.
- In Line 16 the authors use M to set the strategies of the players in NG(W ): Every player j ∈ NG(W ) is matched to the topic associated with the node M (j) ∈ W .

Conclusion

- Focus on the first time a non-empty saturated set W was found in Line 13, and denote the iteration index by t .
- The following Lemma 1 shows that as long as a bipartite G satisfies Hall’s marriage condition, the authors can find the maximum saturated set W efficiently.
- The authors suggest that a “you wrote this, would you be interested in writing on that?” feature could be revolutionary as well—contributing to better social welfare of content consumers, as well as the utility of content providers.

Summary

- Recommendation systems (RSs hereinafter) play a major role in the life nowadays. Many modern RSs, like YouTube, Medium, or Spotify, recommend content created by others and go far beyond recommendations.
- These fluctuations change the utility of the providers and the social welfare of the consumers, defined as the quality of their proposed content.
- Given the demand for topic k, a strategy profile a, and the quality Q of the blogs for the selected topics in a, the recommendation function R recommends content, possibly in a randomized manner.
- The authors represent a game as a tuple P, T , D, Q, C, R, U , where P is the authors, T is the topics, D is the demand for topics, Q and C are the quality and conversion matrices, R is the recommendation function, and U is the utility function.
- Let Hk(a) denote the set of authors whose documents have the highest quality among those who write on topic k under
- For the purpose of social welfare maximization, it suffices to consider candidate profiles in which every topic is selected by at most one author.
- If the authors create several copies of the same topic, high-quality players would block low-quality authors matched to it.
- Tis the set of unmatched topics; Lk is a lower bound on the load on topic k, namely the ongoing number of players the authors matched to it; X, Y and E are the elements of the bipartite graph G (Y stores the set of unmatched players); and a∗ is a non-valid, empty profile that the authors construct as the algorithm advances.
- The authors first find the set of highest-quality players for every topic k, denoted Ak (Line 7).
- For every topic k, the authors consider the set of most profitable players w.r.t. k and their potential utility if matched to k.
- In Line 16 the authors use M to set the strategies of the players in NG(W ): Every player j ∈ NG(W ) is matched to the topic associated with the node M (j) ∈ W .
- Focus on the first time a non-empty saturated set W was found in Line 13, and denote the iteration index by t .
- The following Lemma 1 shows that as long as a bipartite G satisfies Hall’s marriage condition, the authors can find the maximum saturated set W efficiently.
- The authors suggest that a “you wrote this, would you be interested in writing on that?” feature could be revolutionary as well—contributing to better social welfare of content consumers, as well as the utility of content providers.

Related work

- Strikingly, content provider welfare and their fair treatment were only suggested very recently in the Recommendation Systems and Information Retrieval communities [12, 14, 18, 35, 40, 46]. All of these works do not model the incentives of content providers explicitly, and consequently cannot offer a what-if analysis like ours.

Our model is similar to those employed in several recent papers [4, 5, 7, 8, 30]. Ben-Porat et al [8] study a model that is a special case of ours, and show that every learning dynamic converges. Our Theorem 1 recovers and extends their convergence results. Moreover, unlike this work, they do not address convergence time, social welfare, and centralized equilibrium computation. Other works [5, 7, 30] aim to design recommendation mechanisms that mitigate strategic behavior and lead to long-term welfare. On the negative side, their mechanisms might knowingly recommend inferior content to some consumers. We see their work as parallel to ours, as in this work we focus on the prevailing recommendation approach—recommending the best-fitting content. We suggest that a centralized approach, in which the RS orchestrates the player-topic matching, can significantly improve the time until the system reaches stability (in the form of equilibrium). Furthermore, we envision that our approach can also lead to high social welfare, as we discuss in Section 5.

Funding

- Ben-Porat is partially funded by a PhD fellowship from JPMorgan Chase & Co
- Tennenholtz is funded by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement n◦ 740435)

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