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20242026
most citedOneRec: Unifying Retrieve and Rank with Generative Recommender and Iterative Preference Alignment

5 citations · 6 across the 18 of their papers we have counts for

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Showing 2025 · cs.IRShow all

7 papers · 2 filters

cs.IR2025★ 1 cited

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…

cs.IR2025

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…

cs.IR2025

OneLoc: Geo-Aware Generative Recommender Systems for Local Life Service

Zhipeng Wei, Kuo Cai, Junda She +8

Local life service is a vital scenario in Kuaishou App, where video recommendation is intrinsically linked with store's location information. Thus, recommendation in our scenario i…

cs.IR2025

MISS: Multi-Modal Tree Indexing and Searching with Lifelong Sequential Behavior for Retrieval Recommendation

Chengcheng Guo, Junda She, Kuo Cai +5

Large-scale industrial recommendation systems typically employ a two-stage paradigm of retrieval and ranking to handle huge amounts of information. Recent research focuses on impro…

cs.IR2025

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…

cs.IR2025

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…