2 citations · 3 across the 12 of their papers we have counts for
12 papers
SPARC: Sequence-aware Progressive Attribute Routing and Compression Framework for Generative Recommendation
Chang Liu, Changfa Wu, Hui Qian +5
Generative recommendation tokenizes items as discrete Semantic IDs (SIDs) and autoregressively generates target items from users' historical SID sequences. Although existing SIDs i…
Towards Faithful Simulation of Human Shopping Behavior
Jiakai Tang, Yan Mi, Jing Yu +9
Simulating realistic user shopping behavior underpins offline evaluation and reinforcement learning in e-commerce scenarios. While recent LLM- and VLM-based simulators have made en…
Can Generative Recommendation Reach Cold Items? A Temporal Perspective on Semantic-ID Generation
Jie Peng, Yanping Zheng, Zhewei Zhe +3
Semantic-ID-based generative recommendation represents items as sequences of shared semantic tokens, enabling token recombination beyond isolated item IDs. However, closed-world re…
TSGR: Taobao Search Generative Retrieval
Tianyu Zhan, Gui Ling, Tong Xiong +9
Generative retrieval (GR) has demonstrated strong promise for industrial e-commerce search by training a single autoregressive model to directly generate the Semantic IDs (SIDs) of…
Prompt Generation Technical Report
Dan Ou, Gui Ling, Hao Wan +25
Generative retrieval has become an increasingly adopted paradigm for industrial search, recommendation, and advertising systems, delivering significant online gains. Most existing…
LoopMemGR: From Behavior Logs to Evolving Memory for Generative Recommendation
Hui Qian, Changfa Wu, Chang Liu +6
Generative recommendation formulates next-item prediction as conditional autoregressive generation over discrete Semantic IDs, enabling end-to-end recommendation over large-scale i…
Parallel Latent Reasoning for Sequential Recommendation
Jiakai Tang, Xu Chen, Wen Chen +3
Capturing complex user preferences from sparse behavioral sequences remains a fundamental challenge in sequential recommendation. Recent latent reasoning methods have shown promise…
Interactive Recommendation Agent with Active User Commands
Jiakai Tang, Yujie Luo, Xunke Xi +12
Traditional recommender systems rely on passive feedback mechanisms that limit users to simple choices such as like and dislike. However, these coarse-grained signals fail to captu…
RecIS: Sparse to Dense, A Unified Training Framework for Recommendation Models
Hua Zong, Qingtao Zeng, Zhengxiong Zhou +31
In this paper, we propose RecIS, a unified Sparse-Dense training framework designed to achieve two primary goals: 1. Unified Framework To create a Unified sparse-dense training fra…
FORGE: Forming Semantic Identifiers for Generative Retrieval in Industrial Datasets
Kairui Fu, Tao Zhang, Shuwen Xiao +9
Semantic identifiers (SIDs) have gained increasing attention in generative retrieval (GR) for recommendation due to their meaningful semantic discriminability. However, current stu…
Making Pre-trained Language Models Great on Tabular Prediction
Jiahuan Yan, Bo Zheng, Hongxia Xu +5
The transferability of deep neural networks (DNNs) has made significant progress in image and language processing. However, due to the heterogeneity among tables, such DNN bonus is…
Small Models are LLM Knowledge Triggers on Medical Tabular Prediction
Jiahuan Yan, Jintai Chen, Chaowen Hu +4
Recent development in large language models (LLMs) has demonstrated impressive domain proficiency on unstructured textual or multi-modal tasks. However, despite with intrinsic worl…