activity
20242026
collaborators

8 papers

cs.IR2026

Interpretable Representation via LLM-Driven Generative Disentanglement for Local-Life Service Recommendation

Long Zhang, Hao Jiang, Sheng Yu +3

While large language models (LLMs) have advanced ID-based recommendation through Semantic ID (SID) modeling, existing SID generation frameworks largely follow a single-representati…

cs.IR2026

WhisperRec: Latent Reasoning for Efficient Foundation Recommendation Models

Hao Jiang, Peiru Du, Pengfei Yao +10

Large language models (LLMs) have demonstrated strong reasoning capabilities, motivating their adoption as backbones for foundation recommendation models (FRMs). Existing approache…

cs.IR2026

OneReason Technical Report

OneRec Team, Biao Yang, Boyang Ding +81

Generative recommendation models in the OneRec family have been widely deployed in many real-world services, such as short-video, live-streaming, advertising, and e-commerce. Howev…

cs.CL2025

SkipCat: Rank-Maximized Low-Rank Compression of Large Language Models via Shared Projection and Block Skipping

Yu-Chen Lu, Sheng-Feng Yu, Hui-Hsien Weng +5

Large language models (LLM) have achieved remarkable performance across a wide range of tasks. However, their substantial parameter sizes pose significant challenges for deployment…

cs.IR2025

LLM-Aligned Geographic Item Tokenization for Local-Life Recommendation

Hao Jiang, Guoquan Wang, Donglin Zhou +5

Recent advances in Large Language Models (LLMs) have enhanced text-based recommendation by enriching traditional ID-based methods with semantic generalization capabilities. Text-ba…

cs.IR2025

ReST: A Plug-and-Play Spatially-Constrained Representation Enhancement Framework for Local-Life Recommendation

Hao Jiang, Long Zhang, Guoquan Wang +6

Local-life recommendation have witnessed rapid growth, providing users with convenient access to daily essentials. However, this domain faces two key challenges: (1) spatial constr…

cs.CV2025

Boost Self-Supervised Dataset Distillation via Parameterization, Predefined Augmentation, and Approximation

Sheng-Feng Yu, Jia-Jiun Yao, Wei-Chen Chiu

Although larger datasets are crucial for training large deep models, the rapid growth of dataset size has brought a significant challenge in terms of considerable training costs, w…

cs.MM2024

SonicVisionLM: Playing Sound with Vision Language Models

Zhifeng Xie, Shengye Yu, Qile He +1

There has been a growing interest in the task of generating sound for silent videos, primarily because of its practicality in streamlining video post-production. However, existing…