In the recommender system of Meituan Waimai, we are dealing with ever-lengthening user behavior sequences, which pose an increasing challenge to modeling user preference effectively. Existing sequential recommendation models often fail to capture long-term dependencies or are too complex, complicating the fulfillment of Meituan Waimai's unique business needs. To better model user interests, we consider selecting relevant sub-sequences from users' extensive historical behaviors based on their preferences. In this specific scenario, we've noticed that the contexts in which users interact have a significant impact on their preferences. For this purpose, we introduce a novel method called Context-based Fast Recommendation Strategy to tackle the issue of long sequences. We first identify contexts that share similar user preferences with the target context and then locate the corresponding PoIs based on these identified contexts. This approach eliminates the necessity to select a sub-sequence for every candidate PoI, thereby avoiding high time complexity. Specifically, we implement a prototype-based approach to pinpoint contexts that mirror similar user preferences. To amplify accuracy and interpretability, we employ JS divergence of PoI attributes such as categories and prices as a measure of similarity between contexts. A temporal graph integrating both prototype and context nodes helps incorporate temporal information. We then identify appropriate prototypes considering both target contexts and short-term user preferences. Following this, we utilize contexts aligned with these prototypes to generate a sub-sequence, aimed at predicting CTR and CTCVR scores with target attention. Since its inception in 2023, this strategy has been adopted in Meituan Waimai's display recommender system, leading to a 4.6% surge in CTR and a 4.2% boost in GMV.

翻译：在美团外卖的推荐系统中，我们面对着不断增长的用户行为序列，这对有效建模用户偏好构成了日益严峻的挑战。现有的序列推荐模型往往难以捕捉长期依赖关系，或者过于复杂，难以满足美团外卖独特的业务需求。为了更好地建模用户兴趣，我们考虑根据用户偏好从其大量历史行为中选择相关的子序列。在此特定场景下，我们注意到用户交互的上下文对其偏好有显著影响。为此，我们提出了一种名为“基于上下文的快速推荐策略”的新方法来解决长序列问题。我们首先识别与目标上下文具有相似用户偏好的上下文，然后基于这些识别出的上下文定位相应的兴趣点（PoI）。该方法消除了为每个候选PoI选择子序列的必要性，从而避免了高时间复杂度。具体来说，我们采用基于原型的方法来定位反映相似用户偏好的上下文。为提高准确性和可解释性，我们利用PoI属性（如类别和价格）的JS散度作为上下文之间相似性的度量。一个整合了原型节点和上下文节点的时间图有助于融入时间信息。然后我们结合目标上下文和短期用户偏好来识别合适的原型。随后，我们利用与这些原型对齐的上下文来生成子序列，旨在通过目标注意力机制预测CTR和CTCVR得分。自2023年提出以来，该策略已应用于美团外卖的展示推荐系统，使得点击率（CTR）提升4.6%，商品交易总额（GMV）提升4.2%。