2 citations · 2 across the 5 of their papers we have counts for
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Not Only NTP: Extending Training Signal Coverage for Generative Recommendation
Changhao Li, Shuli Wang, Junwei Yin +6
Next-Token Prediction (NTP) carries two structural training signal limitations. First, NTP optimizes for single-step prediction only, placing no supervised pressure on learning lon…
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…
Sample Is Feature: Beyond Item-Level, Toward Sample-Level Tokens for Unified Large Recommender Models
Shuli Wang, Junwei Yin, Changhao Li +6
Scaling industrial recommender models has followed two parallel paradigms: \textbf{sample information scaling} -- enriching the information content of each training sample through…
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…