4 citations · 14 across the 29 of their papers we have counts for
19 papers · 1 filter
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…
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…
Multimodal Generative Retrieval Model with Staged Pretraining for Food Delivery on Meituan
Boyu Chen, Tai Guo, Weiyu Cui +4
Multimodal retrieval models are becoming increasingly important in scenarios such as food delivery, where rich multimodal features can meet diverse user needs and enable precise re…
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…