  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.
