Recommender systems have been widely used for various scenarios, such as e-commerce, news, and music, providing online contents to help and enrich users' daily life. Different scenarios hold distinct and unique characteristics, calling for domain-specific investigations and corresponding designed recommender systems. Therefore, in this paper, we focus on food delivery recommendations to unveil unique features in this domain, where users order food online and enjoy their meals shortly after delivery. We first conduct an in-depth analysis on food delivery datasets. The analysis shows that repeat orders are prevalent for both users and stores, and situations' differently influence repeat and exploration consumption in the food delivery recommender systems. Moreover, we revisit the ability of existing situation-aware methods for repeat and exploration recommendations respectively, and find them unable to effectively solve both tasks simultaneously. Based on the analysis and experiments, we have designed two separate recommendation models -- ReRec for repeat orders and ExpRec for exploration orders; both are simple in their design and computation. We conduct experiments on three real-world food delivery datasets, and our proposed models outperform various types of baselines on repeat, exploration, and combined recommendation tasks. This paper emphasizes the importance of dedicated analyses and methods for domain-specific characteristics for the recommender system studies.

翻译：推荐系统已广泛应用于电子商务、新闻、音乐等多种场景，通过提供在线内容来辅助和丰富用户的日常生活。不同场景具有独特且鲜明的特征，需要针对特定领域进行深入研究并设计相应的推荐系统。因此，本文聚焦于餐饮配送推荐场景，揭示该领域中用户在线订餐并在配送后享用美食的独特特性。我们首先对餐饮配送数据集进行了深入分析。分析表明，用户和店铺均普遍存在重复订单行为，且不同场景对餐饮配送推荐系统中的重复消费和探索消费具有差异化影响。此外，我们重新审视了现有情境感知方法在重复推荐和探索推荐任务中的表现，发现这些方法无法同时有效解决这两个任务。基于上述分析与实验，我们设计了两类独立的推荐模型——ReRec（用于重复订单）和ExpRec（用于探索订单），两者在设计与计算上均保持简洁。我们在三个真实餐饮配送数据集上开展实验，所提出的模型在重复推荐、探索推荐及组合推荐任务中均优于各类基线模型。本文强调了针对推荐系统研究中领域特异性特征开展专门分析与方法设计的重要性。