论文标题
在现场系统中，会议内的上下文感知饲料建议
Intra-session Context-aware Feed Recommendation in Live Systems
论文作者
论文摘要
饲料建议使用户可以不断浏览项目，直到感到不感兴趣并离开会话，这与传统的推荐方案不同。在会话中，用户决定继续浏览或不实质上影响以后点击的情况。但是，这种类型的暴露偏见通常在大多数饲料推荐研究中被忽略或未明确建模。在本文中，我们将此效果建模为会议内环境的一部分，并提出了一种新颖的课内背景感知饲料建议（INSCAFER）框架，以同时提高总视图和总点击。用户点击和浏览决策是通过多任务设置共同学习的，并且会议内上下文由会话揭示的项目序列编码。我们在线部署所有关键业务基准的在线部署。我们的方法在饲料推荐研究中阐明了一些灯光，旨在优化会话级点击并查看指标。
Feed recommendation allows users to constantly browse items until feel uninterested and leave the session, which differs from traditional recommendation scenarios. Within a session, user's decision to continue browsing or not substantially affects occurrences of later clicks. However, such type of exposure bias is generally ignored or not explicitly modeled in most feed recommendation studies. In this paper, we model this effect as part of intra-session context, and propose a novel intra-session Context-aware Feed Recommendation (INSCAFER) framework to maximize the total views and total clicks simultaneously. User click and browsing decisions are jointly learned by a multi-task setting, and the intra-session context is encoded by the session-wise exposed item sequence. We deploy our model online with all key business benchmarks improved. Our method sheds some lights on feed recommendation studies which aim to optimize session-level click and view metrics.
