15 citations · 16 across the 5 of their papers we have counts for
6 papers
PailitaoGR: Latent Think-with-Images for Generative Image Retrieval
Xiaomeng Fan, Yueran Liu, Shengyu Zhou +6
Generative retrieval has demonstrated strong performance by directly generating product semantic identifiers (SIDs). Extending this paradigm to image search, however, is nontrivial…
LoFT-LLM: Low-Frequency Time-Series Forecasting with Large Language Models
Jiacheng You, Jingcheng Yang, Yuhang Xie +7
Time-series forecasting in real-world applications such as finance and energy often faces challenges due to limited training data and complex, noisy temporal dynamics. Existing dee…
NEZHA: A Zero-sacrifice and Hyperspeed Decoding Architecture for Generative Recommendations
Yejing Wang, Shengyu Zhou, Jinyu Lu +9
Generative Recommendation (GR), powered by Large Language Models (LLMs), represents a promising new paradigm for industrial recommender systems. However, their practical applicatio…
GFlowGR: Fine-tuning Generative Recommendation Frameworks with Generative Flow Networks
Yejing Wang, Shengyu Zhou, Jinyu Lu +9
Generative recommendations (GR), which usually include item tokenizers and generative Large Language Models (LLMs), have demonstrated remarkable success across a wide range of scen…
VALUE: Value-Aware Large Language Model for Query Rewriting via Weighted Trie in Sponsored Search
Xiao Zhang, Guanyu Chen, Boyang Zuo +4
Query-to-bidword(i.e., bidding keyword) rewriting is fundamental to sponsored search, transforming noisy user queries into semantically relevant and commercially valuable keywords.…
APG: Adaptive Parameter Generation Network for Click-Through Rate Prediction
Bencheng Yan, Pengjie Wang, Kai Zhang +4
In many web applications, deep learning-based CTR prediction models (deep CTR models for short) are widely adopted. Traditional deep CTR models learn patterns in a static manner, i…