用聚合用户数据做推荐，同时降低用户流失风险。
Modeling Churn in Recommender Systems with Aggregated Preferences
- 基于用户类型和内容满意度的先验概率建模
- 策略在有限时间内自动从探索转向利用
- 适合隐私受限场景下的推荐系统设计
传统推荐系统依赖大量个体用户数据，但监管和技术变革推动其转向使用聚合用户信息。这导致系统需进行大量探索以识别偏好，却可能因推荐不准确引发用户流失。本文提出一种新模型，假设系统对用户类型及各类内容的聚合满意度具有概率先验。我们证明最优策略在有限时间内自然由探索转为利用，并开发了分支定界算法求解该策略，实验证明其有效性。
原文摘要 · Abstract (English)
While recommender systems (RSs) traditionally rely on extensive individual user data, regulatory and technological shifts necessitate reliance on aggregated user information. This shift significantly impacts the recommendation process, requiring RSs to engage in intensive exploration to identify user preferences. However, this approach risks user churn due to potentially unsatisfactory recommendations. In this paper, we propose a model that addresses the dual challenges of leveraging aggregated user information and mitigating churn risk. Our model assumes that the RS operates with a probabilistic prior over user types and aggregated satisfaction levels for various content types. We demonstrate that optimal policies naturally transition from exploration to exploitation in finite time, develop a branch-and-bound algorithm for computing these policies, and empirically validate its effectiveness.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。