1 citations · 2 across the 29 of their papers we have counts for
21 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.…
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…
Break the Inaccessible Boundary: Distilling Post-Conversion Content for User Retention Modeling
Tianbao Ma, Ruochen Yang, Chengen Li +7
User retention is a key metric to measure long-term engagement in modern platforms. In real-time bidding (RTB) advertising system for user re-engagement, the retention model is req…
Harmonizing Generative Retrieval and Ranking in Chain-of-Recommendation
Yu Liu, Jiangxia Cao
Generative recommender systems have recently emerged as a promising paradigm by formulating next-item prediction as an auto-regressive semantic IDs generation, such as OneRec serie…
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).…
OneMall: One Architecture, More Scenarios -- End-to-End Generative Recommender Family at Kuaishou E-Commerce
Kun Zhang, Jingming Zhang, Wei Cheng +29
In the wave of generative recommendation, we present OneMall, an end-to-end generative recommendation framework tailored for e-commerce services at Kuaishou. Our OneMall systematic…
PushGen: Push Notifications Generation with LLM
Shifu Bie, Jiangxia Cao, Zixiao Luo +9
We present PushGen, an automated framework for generating high-quality push notifications comparable to human-crafted content. With the rise of generative models, there is growing…
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…
ChorusCVR: Chorus Supervision for Entire Space Post-Click Conversion Rate Modeling
Wei Cheng, Yucheng Lu, Boyang Xia +9
Post-click conversion rate (CVR) estimation is a vital task in many recommender systems of revenue businesses, e.g., e-commerce and advertising. In a perspective of sample, a typic…
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…
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…
QARM: Quantitative Alignment Multi-Modal Recommendation at Kuaishou
Xinchen Luo, Jiangxia Cao, Tianyu Sun +17
In recent years, with the significant evolution of multi-modal large models, many recommender researchers realized the potential of multi-modal information for user interest modeli…
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…
MARM: Unlocking the Future of Recommendation Systems through Memory Augmentation and Scalable Complexity
Xiao Lv, Jiangxia Cao, Shijie Guan +7
Scaling-law has guided the language model designing for past years, however, it is worth noting that the scaling laws of NLP cannot be directly applied to RecSys due to the followi…
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…
HoME: Hierarchy of Multi-Gate Experts for Multi-Task Learning at Kuaishou
Xu Wang, Jiangxia Cao, Zhiyi Fu +2
In this paper, we present the practical problems and the lessons learned at short-video services from Kuaishou. In industry, a widely-used multi-task framework is the Mixture-of-Ex…