4 citations · 6 across the 9 of their papers we have counts for
11 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…
Discrimination Is Generation: Unifying Ranking and Retrieval from a Tokenizer Perspective
Shuli Wang
Semantic IDs (SIDs) define the generation space of generative recommendation and directly determine its personalization ceiling. However, existing tokenizers are trained independen…
Deep Situation-Aware Interaction Network for Click-Through Rate Prediction
Yimin Lv, Shuli Wang, Beihong Jin +6
User behavior sequence modeling plays a significant role in Click-Through Rate (CTR) prediction on e-commerce platforms. Except for the interacted items, user behaviors contain ric…
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…