  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%.
