用贝叶斯方法同时解决搜索系统中新物品冷启动和用户行为变化问题。
BayesCNS: A Unified Bayesian Approach to Address Cold Start and Non-Stationarity in Search Systems at Scale
- 基于在线学习更新用户-物品交互的先验分布，持续适应新数据。
- 线上实验显示新物品点击率提升10.60%，整体成功率提高1.05%。
- 适合大规模搜索推荐系统中应对冷启动与动态行为变化的场景。
搜索与推荐平台中的信息检索（IR）系统常使用学习排序（LTR）模型对项目进行排序，这些模型严重依赖用户交互特征，如点击和参与数据。这种依赖导致新项目缺乏互动时出现冷启动问题，并难以适应用户行为随时间发生的非平稳变化。我们将其统一为在线学习问题，提出贝叶斯方法 BayesCNS，以在大规模搜索系统中同时应对冷启动和非平稳分布漂移。BayesCNS 通过估计用户-物品交互的先验分布，并利用在线收集的新用户交互数据持续更新，该过程由排序模型引导，借助排序器提供的上下文信息高效探索相关项目。我们在大规模搜索系统中成功部署了 BayesCNS，通过全面的离线与在线实验验证其有效性。其中，线上 A/B 实验表明，新项目互动量提升了 10.60%，整体成功指标提升 1.05%，优于现有生产基线。
原文摘要 · Abstract (English)
Information Retrieval (IR) systems used in search and recommendation platforms frequently employ Learning-to-Rank (LTR) models to rank items in response to user queries. These models heavily rely on features derived from user interactions, such as clicks and engagement data. This dependence introduces cold start issues for items lacking user engagement and poses challenges in adapting to non-stationary shifts in user behavior over time. We address both challenges holistically as an online learning problem and propose BayesCNS, a Bayesian approach designed to handle cold start and non-stationary distribution shifts in search systems at scale. BayesCNS achieves this by estimating prior distributions for user-item interactions, which are continuously updated with new user interactions gathered online. This online learning procedure is guided by a ranker model, enabling efficient exploration of relevant items using contextual information provided by the ranker. We successfully deployed BayesCNS in a large-scale search system and demonstrated its efficacy through comprehensive offline and online experiments. Notably, an online A/B experiment showed a 10.60% increase in new item interactions and a 1.05% improvement in overall success metrics over the existing production baseline.
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