Integrated recommendation, which aims at jointly recommending heterogeneous items from different channels in a main feed, has been widely applied to various online platforms. Though attractive, integrated recommendation requires the ranking methods to migrate from conventional user-item models to the new user-channel-item paradigm in order to better capture users' preferences on both item and channel levels. Moreover, practical feed recommendation systems usually impose exposure constraints on different channels to ensure user experience. This leads to greater difficulty in the joint ranking of heterogeneous items. In this paper, we investigate the integrated recommendation task with exposure constraints in practical recommender systems. Our contribution is forth-fold. First, we formulate this task as a binary online linear programming problem and propose a two-layer framework named Multi-channel Integrated Recommendation with Exposure Constraints (MIREC) to obtain the optimal solution. Second, we propose an efficient online allocation algorithm to determine the optimal exposure assignment of different channels from a global view of all user requests over the entire time horizon. We prove that this algorithm reaches the optimal point under a regret bound of $ \mathcal{O}(\sqrt{T}) $ with linear complexity. Third, we propose a series of collaborative models to determine the optimal layout of heterogeneous items at each user request. The joint modeling of user interests, cross-channel correlation, and page context in our models aligns more with the browsing nature of feed products than existing models. Finally, we conduct extensive experiments on both offline datasets and online A/B tests to verify the effectiveness of MIREC. The proposed framework has now been implemented on the homepage of Taobao to serve the main traffic.

翻译：集成推荐旨在主信息流中联合推荐来自不同渠道的异构物品，已广泛应用于各类在线平台。尽管具有吸引力，但集成推荐要求排序方法从传统的用户-物品范式迁移至新型用户-渠道-物品范式，以便更精准地捕捉用户在物品及渠道层面的偏好。此外，实际信息流推荐系统通常对不同渠道施加曝光约束以保证用户体验，这加剧了异构物品联合排序的难度。本文研究了实际推荐系统中带曝光约束的集成推荐任务，主要贡献包含四方面：第一，将该任务形式化为一个二元在线线性规划问题，并提出名为带曝光约束的多通道集成推荐（MIREC）的两层框架以获取最优解；第二，提出高效在线分配算法，从整个时间范围内所有用户请求的全局视角确定不同渠道的最优曝光分配，证明该算法在$ \mathcal{O}(\sqrt{T}) $的遗憾界下达到最优且具有线性复杂度；第三，提出系列协同模型确定每次用户请求中异构物品的最优布局，模型对用户兴趣、跨渠道相关性及页面上下文的联合建模比现有模型更契合信息流产品的浏览特性；最后，通过离线数据集与在线A/B测试开展大量实验验证MIREC的有效性。该框架现已在淘宝首页部署并承担主要流量服务。