5 citations · 7 across the 20 of their papers we have counts for
23 papers
OneLatent: 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 use as the backbone of foundation recommendation models (FRMs). Existing methods enha…
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…
From Extraction to Navigation: Progressive Retrieval with Indirectly Infinite Depth
Linxiao Che, Shanshan Huang, Haitao Lu +6
Modern large-scale recommender retrieval is shifting from static similarity matching to dynamic item space navigation, framing retrieval as iterative goal-driven graph traversal. C…
POEM: Partial-Order Enhanced Real-Time Sequential Modeling for Recommendation
Linxiao Che, Yijia Sun, Siyuan Lou +5
Real-time recommendation systems suffer from the dynamic drift of user interests and varying contextual conditions. Conventional sequential recommendation models only exploit stati…
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…
MuonRec: Shifting the Optimizer Paradigm Beyond Adam in Scalable Generative Recommendation
Rong Shan, Aofan Yu, Bo Chen +7
Recommender systems (RecSys) are increasingly emphasizing scaling, leveraging larger architectures and more interaction data to improve personalization. Yet, despite the optimizer'…
GEMs: Breaking the Long-Sequence Barrier in Generative Recommendation with a Multi-Stream Decoder
Yu Zhou, Chengcheng Guo, Kuo Cai +6
While generative recommendations (GR) possess strong sequential reasoning capabilities, they face significant challenges when processing extremely long user behavior sequences: the…
OneLive: Dynamically Unified Generative Framework for Live-Streaming Recommendation
Shen Wang, Yusheng Huang, Ruochen Yang +15
Live-streaming recommender system serves as critical infrastructure that bridges the patterns of real-time interactions between users and authors. Similar to traditional industrial…
Coarse-to-Fine Long-term Interest Modeling for Generative Recommendation
Shiteng Cao, Junda She, Bin Zeng +9
Leveraging long-term user behavioral patterns is a key trajectory for enhancing the accuracy of modern recommender systems. While generative recommender systems have emerged as a t…
Unleashing the Native Recommendation Potential: LLM-Based Generative Recommendation via Structured Term Identifiers
Zhiyang Zhang, Junda She, Kuo Cai +8
Leveraging the vast open-world knowledge and understanding capabilities of Large Language Models (LLMs) to develop general-purpose, semantically-aware recommender systems has emerg…
PROMISE: Process Reward Models Unlock Test-Time Scaling Laws in Generative Recommendations
Chengcheng Guo, Kuo Cai, Yu Zhou +5
Generative Recommendation has emerged as a promising paradigm, reformulating recommendation as a sequence-to-sequence generation task over hierarchical Semantic IDs. However, exist…
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…
DiffGRM: Diffusion-based Generative Recommendation Model
Zhao Liu, Yichen Zhu, Yiqing Yang +7
Generative recommendation (GR) is an emerging paradigm that represents each item via a tokenizer as an n-digit semantic ID (SID) and predicts the next item by autoregressively gene…
GRank: Towards Target-Aware and Streamlined Industrial Retrieval with a Generate-Rank Framework
Yijia Sun, Shanshan Huang, Zhiyuan Guan +4
Industrial-scale recommender systems rely on a cascade pipeline in which the retrieval stage must return a high-recall candidate set from billions of items under tight latency. Exi…
MPFormer: Adaptive Framework for Industrial Multi-Task Personalized Sequential Retriever
Yijia Sun, Shanshan Huang, Linxiao Che +4
Modern industrial recommendation systems encounter a core challenge of multi-stage optimization misalignment: a significant semantic gap exists between the multi-objective optimiza…
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…
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…
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…
FIM: Frequency-Aware Multi-View Interest Modeling for Local-Life Service Recommendation
Guoquan Wang, Qiang Luo, Weisong Hu +4
People's daily lives involve numerous periodic behaviors, such as eating and traveling. Local-life platforms cater to these recurring needs by providing essential services tied to…
OneRec: Unifying Retrieve and Rank with Generative Recommender and Iterative Preference Alignment
Jiaxin Deng, Shiyao Wang, Kuo Cai +5
Recently, generative retrieval-based recommendation systems have emerged as a promising paradigm. However, most modern recommender systems adopt a retrieve-and-rank strategy, where…
FARM: Frequency-Aware Model for Cross-Domain Live-Streaming Recommendation
Xiaodong Li, Ruochen Yang, Shuang Wen +10
Live-streaming services have attracted widespread popularity due to their real-time interactivity and entertainment value. Users can engage with live-streaming authors by participa…
CRM: Retrieval Model with Controllable Condition
Chi Liu, Jiangxia Cao, Rui Huang +5
Retrieval models for recommendation need to account for outcomes such as watch time while searching large item corpora efficiently. We present the Controllable Retrieval Model (CRM…
KuaiFormer: Transformer-Based Retrieval at Kuaishou
Chi Liu, Jiangxia Cao, Rui Huang +4
In large-scale content recommendation systems, retrieval serves as the initial stage in the pipeline, responsible for selecting thousands of candidate items from billions of option…