提出可条件识别的环境建模方法，提升分布外推荐的稳定性。
Conditionally Identifiable Latent-Environment Modeling for Out-of-Distribution Recommendation
- 用用户条件指数族建模潜在环境，通过多项式描述其对偏好影响
- 在充分变化条件下，可识别出环境敏感表示，误差越小风险越低
- 适合应对特征、时间、地理等分布外场景的推荐系统研究者
分布外（OOD）推荐易受潜在环境引起的偏好漂移影响。现有方法虽能从日志交互中推断潜在状态，但潜环境的统计意义及其对偏好的影响仍不明确。本文将该问题形式化为条件可识别的风险感知推荐（CI-RR），提出条件可识别潜环境推荐（CILER）。CILER采用用户条件指数族建模潜环境，以特征索引多项式刻画其对偏好的动态影响，并通过对推断环境分布进行边缘化预测。在充分变化、模型正确设定及解码器正则条件下，可将环境敏感表征识别至指定等价类。进一步证明部署日志风险超额值受环境推断误差所限。控制实验验证了充分变化与模型设定的可观测影响。三个数据集上的实验表明，CILER在共享支持范围内，面对特征、时间、地理漂移，均显著提升全部十二项分布外排序指标。
原文摘要 · Abstract (English)
Out-of-distribution (OOD) recommendation is vulnerable to preference shifts induced by a latent environment. Existing methods can infer latent states from logged interactions, yet the statistical meaning of the latent environment and its effect on preference remain underdetermined. We formulate this task as conditionally identifiable risk-aware recommendation (CI-RR) and propose Conditionally Identifiable Latent-Environment Recommendation (CILER). CILER uses a user-conditioned exponential family to model the latent environment and a feature-indexed polynomial to specify how it changes preference. It predicts by marginalizing item probabilities over the inferred environment distribution. Under sufficient variation, correct specification, and decoder regularity, CILER identifies the environment-sensitive representation up to the stated equivalence class. We further bound excess deployment log-risk by environment-inference error. Controlled studies test the observable consequences of sufficient variation and model specification. Experiments on three datasets show that CILER improves all twelve OOD ranking metrics under feature, temporal, and geographical shifts within shared support.
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。