From the 2 of 5 linked papers with an AI index.
5 papers
Sample Is Feature: Beyond Item-Level, Toward Sample-Level Tokens for Unified Large Recommender Models
Shuli Wang, Junwei Yin, Changhao Li +6
The paper introduces SIF, a method that converts each historical user interaction sample into a token using hierarchical group-adaptive quantization and then mixes these tokens wit…
Not Only NTP: Extending Training Signal Coverage for Generative Recommendation
Changhao Li, Shuli Wang, Junwei Yin +6
The paper introduces NONTP, a method that augments next‑token prediction for recommendation models with temporal contrastive learning and trans‑domain learning to capture longer‑ra…
MBGR: Multi-Business Prediction for Generative Recommendation at Meituan
Changhao Li, Junwei Yin, Zhilin Zeng +6
Generative recommendation (GR) has recently emerged as a promising paradigm for industrial recommendations. GR leverages Semantic IDs (SIDs) to reduce the encoding-decoding space a…
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…
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…