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兴趣的餐饮店铺，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。传统推荐通常聚焦于用户对物品的单一偏好，而外卖推荐需综合考量用户对店铺和食物的双重偏好。(2) 时段变化偏好建模。传统推荐一般从会话级或天级视角建模用户偏好的连续变化。然而，在实际外卖系统中，用户在一天中的早晨、中午、傍晚和深夜时段偏好差异显著。为解决这些挑战，我们提出了一种面向外卖推荐的双重时段变化偏好建模方法（DPVP）。具体地，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物的交互捕获其双重偏好。此外，为建模一天中不同时间段的多样化偏好，我们提出了基于时间的分解模块及时间感知门控机制。充分的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先进方法，且能有效建模双重时段变化偏好。此外，该模型已在美团外卖平台上线，带动总商品交易额（GMV）平均提升0.70%。