1 citations · 1 across the 17 of their papers we have counts for
17 papers · 1 filter
From Trajectories to Evidence: Auditable Experimental Records for Industrial Research Agents
Zijie Zhuang, Changxin Lao, Pengbo Xu +13
Research agents increasingly conduct multi-round machine-learning experiments in industrial recommendation settings and retain the resulting trajectories to guide later decisions.…
RecoReward: Recommender-Guided Multimodal Description Generation for Recommendation
Guohong Mu, Yueyang Liu, Jiangxia Cao +8
Multimodal large language models (MLLMs) can convert multimodal item content into structured descriptions used as semantic features for recommendation. Conventional content-only ge…
Reward Guided Decoding for Generative Recommendation
Ruochen Yang, Yusheng Huang, Youfeng Zheng +11
Generative recommendation formulates recommendation task into an SID sequence autoregressive generation paradigm, but the decoding process is often dominated by generation likeliho…
Unifying Generative Recall and Multi-Objective Ranking in a Single Decoder-Only Sequence
Ruochen Yang, Shuang Wen, Pengbo Xu +6
Modern industrial recommendation systems typically separate recall and ranking into two independent stages. Although this cascade supports corpus-level retrieval and fine-grained m…
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…
On the Equivalence Between Auto-Regressive Next Token Prediction and Full-Item-Vocabulary Maximum Likelihood Estimation in Generative Recommendation--A Short Note
Yusheng Huang, Shuang Yang, Zhaojie Liu +1
Generative recommendation (GR) has emerged as a widely adopted paradigm in industrial sequential recommendation. Current GR systems follow a similar pipeline: tokenization for item…
From Agnostic to Specific: Latent Preference Diffusion for Multi-Behavior Sequential Recommendation
Ruochen Yang, Xiaodong Li, Jiawei Sheng +6
Multi-behavior sequential recommendation (MBSR) aims to learn the dynamic and heterogeneous interactions of users' multi-behavior sequences, so as to capture user preferences under…
MaRI: Accelerating Ranking Model Inference via Structural Re-parameterization in Large Scale Recommendation System
Yusheng Huang, Pengbo Xu, Shen Wang +7
Ranking models, i.e., coarse-ranking and fine-ranking models, serve as core components in large-scale recommendation systems, responsible for scoring massive item candidates based…
SARM: LLM-Augmented Semantic Anchor for End-to-End Live-Streaming Ranking
Ruochen Yang, Yueyang Liu, Zijie Zhuang +14
Large-scale live-streaming recommendation requires precise modeling of non-stationary content semantics under strict real-time serving constraints. In industrial deployment, two co…
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…
QARM V2: Quantitative Alignment Multi-Modal Recommendation for Reasoning User Sequence Modeling
Tian Xia, Jiaqi Zhang, Yueyang Liu +25
With the evolution of large language models (LLMs), there is growing interest in leveraging their rich semantic understanding to enhance industrial recommendation systems (RecSys).…
Foresight Prediction Enhanced Live-Streaming Recommendation
Jiangxia Cao, Ruochen Yang, Xiang Chen +8
Live-streaming, as an emerging media enabling real-time interaction between authors and users, has attracted significant attention. Unlike the stable playback time of traditional T…
LLM-Alignment Live-Streaming Recommendation
Yueyang Liu, Jiangxia Cao, Shen Wang +7
In recent years, integrated short-video and live-streaming platforms have gained massive global adoption, offering dynamic content creation and consumption. Unlike pre-recorded sho…
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…
LiveForesighter: Generating Future Information for Live-Streaming Recommendations at Kuaishou
Yucheng Lu, Jiangxia Cao, Xu Kuan +6
Live-streaming, as a new-generation media to connect users and authors, has attracted a lot of attention and experienced rapid growth in recent years. Compared with the content-sta…
A Unified Framework for Cross-Domain Recommendation
Jiangxia Cao, Shen Wang, Gaode Chen +4
In addressing the persistent challenges of data-sparsity and cold-start issues in domain-expert recommender systems, Cross-Domain Recommendation (CDR) emerges as a promising method…
Moment&Cross: Next-Generation Real-Time Cross-Domain CTR Prediction for Live-Streaming Recommendation at Kuaishou
Jiangxia Cao, Shen Wang, Yue Li +6
Kuaishou, is one of the largest short-video and live-streaming platform, compared with short-video recommendations, live-streaming recommendation is more complex because of: (1) te…