most citedAn End-to-End Multi-objective Ensemble Ranking Framework for Video Recommendation

1 citations · 1 across the 4 of their papers we have counts for

collaborators

6 papers

cs.SE2026

AdNanny: One Reasoning LLM for All Offline Ads Recommendation Tasks

Nan Hu, Han Li, Jimeng Sun +16

Large Language Models (LLMs) have shown strong capabilities in Natural Language Understanding and Generation, but deploying them directly in online advertising systems is often imp…

cs.IR2026

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…

cs.IR2026

Towards End-to-End Alignment of User Satisfaction via Questionnaire in Video Recommendation

Na Li, Jiaqi Yu, Minzhi Xie +8

Short-video recommender systems typically optimize ranking models using dense user behavioral signals, such as clicks and watch time. However, these signals are only indirect proxi…

cs.IR20251 cited

An End-to-End Multi-objective Ensemble Ranking Framework for Video Recommendation

Tiantian He, Minzhi Xie, Runtong Li +6

We propose a novel End-to-end Multi-objective Ensemble Ranking framework (EMER) for the multi-objective ensemble ranking module, which is the most critical component of the short v…

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…