Beyond Self-Interest: How Group Strategies Reshape Content Creation in Recommendation Platforms?

Yaolong Yu, Fan Yao, Sinno Jialin Pan
Proceedings of the 42nd International Conference on Machine Learning, PMLR 267:73326-73349, 2025.

Abstract

We employ a game-theoretic framework to study the impact of a specific strategic behavior among creators—group behavior—on recommendation platforms. In this setting, creators within a group collaborate to maximize their collective utility. We show that group behavior has a limited effect on the game’s equilibrium when the group size is small. However, when the group size is large, group behavior can significantly alter content distribution and user welfare. Specifically, in a top-$K$ recommendation system with exposure-based rewards, we demonstrate that user welfare can suffer a significant loss due to group strategies, and user welfare does not necessarily increase with larger values of $K$ or more random matching, contrasting sharply with the individual creator case. Furthermore, we investigate user welfare guarantees through the lens of the Price of Anarchy (PoA). In the general case, we establish a negative result on the bound of PoA with exposure rewards, proving that it can be arbitrarily large. We then investigate a user engagement rewarding mechanism, which mitigates the issues caused by large group behavior, showing that $\text{PoA}\leq K+1$ in the general case and $\text{PoA}\leq 2$ in the binary case. Empirical results from simulations further support the effectiveness of the user engagement rewarding mechanism.

Cite this Paper

BibTeX
@InProceedings{pmlr-v267-yu25t, title = {Beyond Self-Interest: How Group Strategies Reshape Content Creation in Recommendation Platforms?}, author = {Yu, Yaolong and Yao, Fan and Pan, Sinno Jialin}, booktitle = {Proceedings of the 42nd International Conference on Machine Learning}, pages = {73326--73349}, year = {2025}, editor = {Singh, Aarti and Fazel, Maryam and Hsu, Daniel and Lacoste-Julien, Simon and Berkenkamp, Felix and Maharaj, Tegan and Wagstaff, Kiri and Zhu, Jerry}, volume = {267}, series = {Proceedings of Machine Learning Research}, month = {13--19 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v267/main/assets/yu25t/yu25t.pdf}, url = {https://proceedings.mlr.press/v267/yu25t.html}, abstract = {We employ a game-theoretic framework to study the impact of a specific strategic behavior among creators—group behavior—on recommendation platforms. In this setting, creators within a group collaborate to maximize their collective utility. We show that group behavior has a limited effect on the game’s equilibrium when the group size is small. However, when the group size is large, group behavior can significantly alter content distribution and user welfare. Specifically, in a top-$K$ recommendation system with exposure-based rewards, we demonstrate that user welfare can suffer a significant loss due to group strategies, and user welfare does not necessarily increase with larger values of $K$ or more random matching, contrasting sharply with the individual creator case. Furthermore, we investigate user welfare guarantees through the lens of the Price of Anarchy (PoA). In the general case, we establish a negative result on the bound of PoA with exposure rewards, proving that it can be arbitrarily large. We then investigate a user engagement rewarding mechanism, which mitigates the issues caused by large group behavior, showing that $\text{PoA}\leq K+1$ in the general case and $\text{PoA}\leq 2$ in the binary case. Empirical results from simulations further support the effectiveness of the user engagement rewarding mechanism.} }
Endnote
%0 Conference Paper %T Beyond Self-Interest: How Group Strategies Reshape Content Creation in Recommendation Platforms? %A Yaolong Yu %A Fan Yao %A Sinno Jialin Pan %B Proceedings of the 42nd International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2025 %E Aarti Singh %E Maryam Fazel %E Daniel Hsu %E Simon Lacoste-Julien %E Felix Berkenkamp %E Tegan Maharaj %E Kiri Wagstaff %E Jerry Zhu %F pmlr-v267-yu25t %I PMLR %P 73326--73349 %U https://proceedings.mlr.press/v267/yu25t.html %V 267 %X We employ a game-theoretic framework to study the impact of a specific strategic behavior among creators—group behavior—on recommendation platforms. In this setting, creators within a group collaborate to maximize their collective utility. We show that group behavior has a limited effect on the game’s equilibrium when the group size is small. However, when the group size is large, group behavior can significantly alter content distribution and user welfare. Specifically, in a top-$K$ recommendation system with exposure-based rewards, we demonstrate that user welfare can suffer a significant loss due to group strategies, and user welfare does not necessarily increase with larger values of $K$ or more random matching, contrasting sharply with the individual creator case. Furthermore, we investigate user welfare guarantees through the lens of the Price of Anarchy (PoA). In the general case, we establish a negative result on the bound of PoA with exposure rewards, proving that it can be arbitrarily large. We then investigate a user engagement rewarding mechanism, which mitigates the issues caused by large group behavior, showing that $\text{PoA}\leq K+1$ in the general case and $\text{PoA}\leq 2$ in the binary case. Empirical results from simulations further support the effectiveness of the user engagement rewarding mechanism.
APA
Yu, Y., Yao, F. & Pan, S.J.. (2025). Beyond Self-Interest: How Group Strategies Reshape Content Creation in Recommendation Platforms?. Proceedings of the 42nd International Conference on Machine Learning, in Proceedings of Machine Learning Research 267:73326-73349 Available from https://proceedings.mlr.press/v267/yu25t.html.

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