10 citations · 13 across the 32 of their papers we have counts for
7 papers · 1 filter
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…
PIT: A Dynamic Personalized Item Tokenizer for End-to-End Generative Recommendation
Huanjie Wang, Xinchen Luo, Honghui Bao +6
Generative Recommendation has revolutionized recommender systems by reformulating retrieval as a sequence generation task over discrete item identifiers. Despite the progress, exis…
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…
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…
DaRec: A Disentangled Alignment Framework for Large Language Model and Recommender System
Xihong Yang, Heming Jing, Zixing Zhang +10
Benefiting from the strong reasoning capabilities, Large language models (LLMs) have demonstrated remarkable performance in recommender systems. Various efforts have been made to d…