用条件信息提升推荐精准度，让用户更愿意点击
Bootstrapping Conditional Retrieval for User-to-Item Recommendations
- 用物品侧信息作查询条件，实现条件化召回
- 在Pinterest上使活跃用户数提升0.26%
- 适合需要主题驱动推荐的场景
用户到物品的召回是推荐系统中的活跃研究方向，双塔模型因结构简单和部署高效被广泛采用。本文聚焦于一种称为“条件召回”的变体，要求召回物品与特定条件（如主题）相关。我们提出的方法使用与标准双塔模型相同的训练数据，但在查询端引入物品侧信息作为条件，从而实现新条件召回场景的快速搭建，并促进用户与条件之间的特征交互。实验表明，该方法能有效召回高度相关的物品，在参与度指标上优于标准双塔模型。所提模型已部署于Pinterest的主题通知流中，带来每周活跃用户增长0.26%。
原文摘要 · Abstract (English)
User-to-item retrieval has been an active research area in recommendation system, and two tower models are widely adopted due to model simplicity and serving efficiency. In this work, we focus on a variant called \textit{conditional retrieval}, where we expect retrieved items to be relevant to a condition (e.g. topic). We propose a method that uses the same training data as standard two tower models but incorporates item-side information as conditions in query. This allows us to bootstrap new conditional retrieval use cases and encourages feature interactions between user and condition. Experiments show that our method can retrieve highly relevant items and outperforms standard two tower models with filters on engagement metrics. The proposed model is deployed to power a topic-based notification feed at Pinterest and led to +0.26\% weekly active users.
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。