9 papers
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…
Ling and Ring 2.6 Technical Report: Efficient and Instant Agentic Intelligence at Trillion-Parameter Scale
Ang Li, Ben Liu, Bin Han +215
Efficient and scalable agentic intelligence requires models that can deliver both low-latency responses and strong reasoning capabilities while remaining practical to train, serve,…
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…
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…
You Only Evaluate Once: A Tree-based Rerank Method at Meituan
Shuli Wang, Yinqiu Huang, Changhao Li +6
Reranking plays a crucial role in modern recommender systems by capturing the mutual influences within the list. Due to the inherent challenges of combinatorial search spaces, most…
Generative Bid Shading in Real-Time Bidding Advertising
Yinqiu Huang, Hao Ma, Wenshuai Chen +7
Bid shading plays a crucial role in Real-Time Bidding (RTB) by adaptively adjusting the bid to avoid advertisers overspending. Existing mainstream two-stage methods, which first mo…