Takeaway recommender systems, which aim to accurately provide stores that offer foods meeting users' interests, have served billions of users in our daily life. Different from traditional recommendation, takeaway recommendation faces two main challenges: (1) Dual Interaction-Aware Preference Modeling. Traditional recommendation commonly focuses on users' single preferences for items while takeaway recommendation needs to comprehensively consider users' dual preferences for stores and foods. (2) Period-Varying Preference Modeling. Conventional recommendation generally models continuous changes in users' preferences from a session-level or day-level perspective. However, in practical takeaway systems, users' preferences vary significantly during the morning, noon, night, and late night periods of the day. To address these challenges, we propose a Dual Period-Varying Preference modeling (DPVP) for takeaway recommendation. Specifically, we design a dual interaction-aware module, aiming to capture users' dual preferences based on their interactions with stores and foods. Moreover, to model various preferences in different time periods of the day, we propose a time-based decomposition module as well as a time-aware gating mechanism. Extensive offline and online experiments demonstrate that our model outperforms state-of-the-art methods on real-world datasets and it is capable of modeling the dual period-varying preferences. Moreover, our model has been deployed online on Meituan Takeaway platform, leading to an average improvement in GMV (Gross Merchandise Value) of 0.70%.

翻译：外卖推荐系统旨在精准提供符合用户偏好的餐饮店铺，已服务于日常生活中的数十亿用户。与传统推荐不同，外卖推荐面临两大挑战：（1）双重交互感知偏好建模。传统推荐通常聚焦用户对物品的单一偏好，而外卖推荐需综合考虑用户对店铺和食品的双重偏好。（2）时段变化偏好建模。传统推荐一般从会话级或天级维度建模用户偏好的连续变化，但在实际外卖系统中，用户偏好会在清晨、中午、夜晚及深夜等不同时段发生显著变化。针对这些挑战，我们提出面向外卖推荐的双重时段变化偏好建模（DPVP）方法。具体而言，我们设计了一个双重交互感知模块，旨在基于用户与店铺和食品的交互捕获其双重偏好。此外，为建模一天内不同时间段的多样化偏好，我们提出基于时间的分解模块与时序感知门控机制。大量离线和在线实验表明，我们的模型在真实数据集上优于现有最优方法，并能有效建模双重时段变化偏好。值得注意的是，该模型已在美团外卖平台上线部署，实现了商品交易总额（GMV）平均提升0.70%。