竞争型内容创作者场景下Top-$K$推荐的性能有多差？
How Bad is Top-$K$ Recommendation under Competing Content Creators?
AI总结：
该研究针对推荐平台上内容创作者竞争影响用户福利的问题，基于top-$K$推荐、随机效用模型和无遗憾学习假设建模，通过无政府状态价格证明用户福利损失存在小常数上界，揭示了相关性驱动推荐的长期内在优势。
摘要：
内容创作者在推荐平台上争夺曝光量，这类策略行为会导致内容分布发生动态变化。然而，创作者之间的竞争如何影响用户福利，以及以相关性为驱动的推荐在长期内如何作用于这种动态变化，目前在很大程度上仍不明确。\n 本研究为上述研究问题提供了理论洞见。我们基于以下假设对创作者竞争进行建模：1）平台采用无偏的top-$K$推荐策略；2）用户决策遵循随机效用（Random Utility）模型；3）内容创作者为争夺用户参与度展开竞争，且在事后无法知晓自身效用函数的情况下，采用任意无遗憾（no-regret）学习算法更新自身策略。我们通过无政府状态价格（Price of Anarchy）视角研究用户福利保障，证明由创作者竞争导致的用户福利损失比例始终存在一个取决于$K$值与用户决策随机性的小常数上界；同时我们也证明了该上界的紧性。研究结果揭示了短视推荐方法的内在优势：只要用户决策包含随机性，且平台为用户提供数量合理的备选内容，以相关性为驱动的匹配在长期内的表现就相当不错。
英文摘要：
Content creators compete for exposure on recommendation platforms, and such strategic behavior leads to a dynamic shift over the content distribution. However, how the creators' competition impacts user welfare and how the relevance-driven recommendation influences the dynamics in the long run are still largely unknown. This work provides theoretical insights into these research questions. We model the creators' competition under the assumptions that: 1) the platform employs an innocuous top-$K$ recommendation policy; 2) user decisions follow the Random Utility model; 3) content creators compete for user engagement and, without knowing their utility function in hindsight, apply arbitrary no-regret learning algorithms to update their strategies. We study the user welfare guarantee through the lens of Price of Anarchy and show that the fraction of user welfare loss due to creator competition is always upper bounded by a small constant depending on $K$ and randomness in user decisions; we also prove the tightness of this bound. Our result discloses an intrinsic merit of the myopic approach to the recommendation, i.e., relevance-driven matching performs reasonably well in the long run, as long as users' decisions involve randomness and the platform provides reasonably many alternatives to its users.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
