Context-based Fast Recommendation Strategy for Long User Behavior Sequence in Meituan Waimai

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. A number of existing sequential recommendation models struggle to capture long-term dependencies, or they exhibit high complexity, both of which make it difficult to satisfy the unique business requirements of Meituan Waimai's recommender system.