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cs.IR2026
DOS: Dual-Flow Orthogonal Semantic IDs for Recommendation in Meituan
Junwei Yin, Senjie Kou, Changhao Li +6
Semantic IDs serve as a key component in generative recommendation systems. They not only incorporate open-world knowledge from large language models (LLMs) but also compress the s…
cs.IR2026
Entire Chain Uplift Modeling with Context-Enhanced Learning for Intelligent Marketing
Yinqiu Huang, Shuli Wang, Min Gao +6
Uplift modeling, vital in online marketing, seeks to accurately measure the impact of various strategies, such as coupons or discounts, on different users by predicting the Individ…
cs.IR2025
NLGR: Utilizing Neighbor Lists for Generative Rerank in Personalized Recommendation Systems
Shuli Wang, Xue Wei, Senjie Kou +6
Reranking plays a crucial role in modern multi-stage recommender systems by rearranging the initial ranking list. Due to the inherent challenges of combinatorial search spaces, som…