Filter Bubbles and Homogenization in Recommender Ecosystems with Strategic Content Producers and Competing Platforms

Agent-based studies of how recommendation algorithms reshape consumption diversity have so far treated the item supply as exogenous and the platform as a single neutral intermediary. Anwar, Schoenebeck, and Dhillon (WWW '24) decomposed the long-run effects of recommenders into inter-user diversity (how much mean consumption differs across users) and intra-user diversity (how varied one user's consumption is), showing that past-consumption-based recommendation alleviates the filter-bubble effect mainly by shrinking inter-user diversity, and left open the extension they themselves propose: incorporate all three agent types — users, platforms, and producers — into such a simulation and examine homogenization and filter-bubble effects in that multifaceted ecosystem. No prior work carries the inter-/intra-user diversity decomposition into an ecosystem with endogenous strategic producers and competing platforms. The open problem is to build and analyze such an ecosystem model and characterize whether the single-platform qualitative findings persist, reverse, or amplify.

Background

Recommender systems — algorithms that select which items of a platform's catalog a user is shown — are widely suspected of reshaping long-run media consumption in two distinct ways. The filter bubble effect is the narrowing of a user's exposure to content matching their existing preferences; homogenization is the convergence of different users' consumption towards similar content. Empirical and simulation studies had generally treated these as two sides of one trade-off: Nguyen et al. (2014) found on MovieLens data that collaborative-filtering-style recommendations expose users to more niche items and decrease filter-bubble-type concentration, and Aridor et al. (2020) reached related conclusions in a simulation study, while Chaney et al. (2018) showed that so-called algorithmic confounding — recommenders learning from their own past influence on behavior — increases homogeneity and decreases utility, and Fleder and Hosanagar (2009) analyzed sales-diversity effects of recommenders on a catalog of exogenous items.

Anwar, Schoenebeck, and Dhillon (WWW '24) refined this picture with an agent-based simulation (a computational model in which individual software agents interact over many rounds and long-run aggregate patterns are measured). Items carry a real-valued quality (universal desirability) and genre (positional attribute); each user carries a latent real-valued genre preference. Users are utility maximizers who must estimate item utility from signals — a private quality signal, the perceived distance between the item's genre and their preference, and a recommendation signal emitted by the platform — using a learned estimator (a linear regression in the reported experiments; a footnote records that a multi-layer perceptron, a standard feed-forward neural network, gave qualitatively the same results). Seven recommendation algorithms are compared: four idealized baselines (no recommendation, or revealing the item's true genre, true quality, or both) and three based on past consumption (consumption counts, SVD, and hybrid recommendation). The authors additionally introduce two novel algorithms, binned consumption-based recommendation and skewed top pick recommendation, aimed at affecting both inter-user and intra-user diversity.

Their measurement contribution is a decomposition of consumption diversity. Write $\mu_j$ for the mean genre of the items user $j$ consumes and $\sigma_j^2$ for the variance of genres within that user's consumption. Inter-user diversity is $\mathrm{Var}_{j} \left[ \mu_j \right]$, the spread of users' mean consumption; intra-user diversity is $\mathbb{E}_{j} \left[ \sigma_j^2 \right]$, how wide a genre range the average individual consumes. The filter-bubble effect is operationalized as the ratio of inter-user to intra-user diversity — a strong bubble means users' consumption is both separated from each other and individually narrow — and homogeneity is measured as the reciprocal of the Euclidean norm of the two diversities, $1 / \sqrt{\mathrm{Var}_{j} \left[ \mu_j \right]^2 + \mathbb{E}_{j} \left[ \sigma_j^2 \right]^2}$, which is high exactly when both diversities are low. Their central finding: past-consumption-based recommenders alleviate the filter-bubble effect essentially only by reducing inter-user diversity (steering everyone towards blockbuster items) without materially increasing intra-user diversity.

The framework has two deliberate structural limits, stated in the paper's discussion. First, content items are exogenously generated: no agent decides what content exists, though in real ecosystems producers strategically create, optimize, and migrate content in response to algorithmic incentives, changing the available item pool. Second, there is a single, neutral platform, whereas in practice multiple platforms with distinct objectives (and genre-specific sorting, e.g., live-streaming versus short-video platforms) compete for users and producers. The paper's own proposed next step is to incorporate all three agent types — users, platforms, and producers — and examine homogenization and filter bubbles in such a multifaceted ecosystem; that extension is the problem posed here. A related modeling literature exists on the producer and platform side but asks different questions: Ben-Porat and Tennenholtz (2018) study recommendation with strategic content providers as a mechanism-design problem of fairness and stability (proposing a Shapley mediator, a solution concept allocating recommendation slots by each provider's marginal contribution); Lada et al. (2022) estimate which content producers will create the highest long-run user value; and Zhang and Zhang (2024) simulate a two-sided content market (consumers and creators, with generative-AI producers) tracking welfare, inequality, and market concentration. None of these measures consumption diversity through the inter-/intra-user decomposition or characterizes the filter-bubble-versus-homogenization dynamics of recommendation algorithms, so the ecosystem question posed by the WWW '24 paper remains unaddressed.

Problem Statement

Extend the agent-based framework of Anwar, Schoenebeck, and Dhillon (WWW '24) — utility-maximizing users with latent genre preferences who estimate item utility from signals including a recommendation signal, items with quality and genre, and the diversity decomposition into inter-user diversity $\mathrm{Var}_{j} \left[ \mu_j \right]$ and intra-user diversity $\mathbb{E}_{j} \left[ \sigma_j^2 \right]$ (filter-bubble effect as their ratio, plus the homogeneity measure) — to a multifaceted ecosystem in which (i) content producers are endogenous strategic agents that create content, optimize against algorithmic and platform incentives, and may migrate across platforms, and (ii) multiple platforms with distinct objectives compete for users and producers. In that ecosystem, characterize the long-run effects of recommendation algorithms on homogenization and filter bubbles: how inter-user diversity, intra-user diversity, the filter-bubble ratio, and homogeneity evolve relative to the single-platform, exogenous-content baseline, and in particular whether the source paper's central qualitative finding — that past-consumption-based recommendations alleviate the filter-bubble effect essentially only by reducing inter-user diversity — persists, reverses, or amplifies when supply and platform structure are endogenous. The specific producer strategy space, platform objectives, and recommendation algorithms compared are design choices of the solver, but the study must measure the stated diversity outcomes and anchor its conclusions against the single-platform baseline.

References

1. Md Sanzeed Anwar, Grant Schoenebeck, and Paramveer S. Dhillon. Filter Bubble or Homogenization? Disentangling the Long-Term Effects of Recommendations on User Consumption Patterns. Proceedings of the ACM Web Conference 2024 (WWW '24), 2024. DOI: 10.1145/3589334.3645497; arXiv:2402.15013. https://doi.org/10.1145/3589334.3645497
2. Allison J. B. Chaney, Brandon M. Stewart, and Barbara E. Engelhardt. How Algorithmic Confounding in Recommendation Systems Increases Homogeneity and Decreases Utility. Proceedings of the 12th ACM Conference on Recommender Systems (RecSys '18), 2018. DOI: 10.1145/3240323.3240370. https://doi.org/10.1145/3240323.3240370
3. Daniel Fleder and Kartik Hosanagar. Blockbuster Culture's Next Rise or Fall: The Impact of Recommender Systems on Sales Diversity. Management Science, 2009. DOI: 10.1287/mnsc.1080.0974. https://doi.org/10.1287/mnsc.1080.0974
4. Tien T. Nguyen, Pik-Mai Hui, F. Maxwell Harper, Loren Terveen, and Joseph A. Konstan. Exploring the Filter Bubble: The Effect of Using Recommender Systems on Online Content Diversity. Proceedings of the 23rd International Conference on World Wide Web (WWW '14 Companion), 2014. DOI: 10.1145/2566486.2568012. https://doi.org/10.1145/2566486.2568012
5. Guy Aridor, Duarte Goncalves, and Shan Sikdar. Deconstructing the Filter Bubble: User Decision-Making and Recommender Systems. Proceedings of the 14th ACM Conference on Recommender Systems (RecSys '20), 2020. DOI: 10.1145/3383313.3412246. https://doi.org/10.1145/3383313.3412246
6. Omer Ben-Porat and Moshe Tennenholtz. A Game-Theoretic Approach to Recommendation Systems with Strategic Content Providers, 2018. arXiv:1806.00955. https://arxiv.org/abs/1806.00955
7. Akos Lada, Xiaoxuan Liu, Jens Rischbieth, Yi Wang, and Yuwen Zhang. Long-run User Value Optimization in Recommender Systems through Content Creation Modeling, 2022. arXiv:2204.11421. https://arxiv.org/abs/2204.11421
8. Yukun Zhang and Tianyang Zhang. The Impact of Generative AI on Content Platforms: A Two-Sided Market Analysis with Multi-Dimensional Quality Heterogeneity, 2024. arXiv:2410.13101. https://arxiv.org/abs/2410.13101
9. Kang Wang, Renzhe Xu, and Bo Li. Lower Bias, Higher Welfare: How Creator Competition Reshapes Bias-Variance Tradeoff in Recommendation Platforms?, 2025. arXiv:2511.20289. https://arxiv.org/abs/2511.20289
10. Sarmad Chandio and Rishab Nithyanand. Examining the Role of YouTube Production and Consumption Dynamics on the Formation of Extreme Ideologies, 2026. arXiv:2603.08049. https://arxiv.org/abs/2603.08049
11. Jiyuan Yang, Gengxin Sun, Mengqi Zhang, Lingjie Wang, Yuanzi Li, Hongxi Cui, Xin Xin, and Pengjie Ren. Do Generative Recommenders Deepen the Information Cocoon? A Closed-Loop Simulation with LLM-powered User Simulators, 2026. arXiv:2606.17707. https://arxiv.org/abs/2606.17707
12. Md Sanzeed Anwar, Paramveer S. Dhillon, and Grant Schoenebeck. Recommendation and Temptation, 2024. arXiv:2412.10595. https://arxiv.org/abs/2412.10595
13. Xiaodan Wang, Yanbin Liu, Shiqing Wu, Ziying Zhao, Yuxuan Hu, Weihua Li, and Quan Bai. Ideological Isolation in Online Social Networks: A Survey of Computational Definitions, Metrics, and Mitigation Strategies, 2026. arXiv:2601.07884. https://arxiv.org/abs/2601.07884