4 papers · 1 filter
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…
DynamicPO: Dynamic Preference Optimization for Recommendation
Xingyu Hu, Kai Zhang, Jiancan Wu +7
In large language model (LLM)-based recommendation systems, direct preference optimization (DPO) effectively aligns recommendations with user preferences, requiring multi-negative…
Next-Scale Generative Reranking: A Tree-based Generative Rerank Method at Meituan
Shuli Wang, Changhao Li, Ke Fan +5
In modern multi-stage recommendation systems, reranking plays a critical role by modeling contextual information. Due to inherent challenges such as the combinatorial space complex…
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…