6 papers
Beyond Positive Signals: Unlocking Implicit Negative Behaviors for Enhanced Sequential User Modeling
Zexuan Cheng, Yue Liu, Jun Zhang +1
User behavior sequence modeling has become a central component in modern click-through rate (CTR) prediction. Over the past years, the community has invested substantial effort int…
OpenOneRec Technical Report
Guorui Zhou, Honghui Bao, Jiaming Huang +44
While the OneRec series has successfully unified the fragmented recommendation pipeline into an end-to-end generative framework, a significant gap remains between recommendation sy…
ICAD-LLM: One-for-All Anomaly Detection via In-Context Learning with Large Language Models
Zhongyuan Wu, Jingyuan Wang, Zexuan Cheng +5
Anomaly detection (AD) is a fundamental task of critical importance across numerous domains. Current systems increasingly operate in rapidly evolving environments that generate div…
OneRec-Think: In-Text Reasoning for Generative Recommendation
Zhanyu Liu, Shiyao Wang, Xingmei Wang +23
The powerful generative capacity of Large Language Models (LLMs) has instigated a paradigm shift in recommendation. However, existing generative models (e.g., OneRec) operate as im…
OneRec-V2 Technical Report
Guorui Zhou, Hengrui Hu, Hongtao Cheng +72
Recent breakthroughs in generative AI have transformed recommender systems through end-to-end generation. OneRec reformulates recommendation as an autoregressive generation task, a…
OneRec Technical Report
Guorui Zhou, Jiaxin Deng, Jinghao Zhang +62
Recommender systems have been widely used in various large-scale user-oriented platforms for many years. However, compared to the rapid developments in the AI community, recommenda…