activity
20232026
most citedTWIN V2: Scaling Ultra-Long User Behavior Sequence Modeling for Enhanced CTR Prediction at Kuaishou

33 citations · 33 across the 3 of their papers we have counts for

collaborators

5 papers

cs.CL2026

Tree-of-Experience: Hierarchical Experience Management for Self-Evolving Agents

Zihao Deng, Yining Zhu, Leiming Wang +2

Continual self-evolution requires LLM agents to transform environmental interactions into reliable and reusable experience. Existing methods typically refine individual trajectorie…

cs.CL2026

FinEvolveBench: A Benchmark for Self-Evolving Agents on Low-Repetition Tasks with Implicit Rewards

Zihao Deng, Yining Zhu, Leiming Wang +6

Experience-based self-evolution enables language-model agents to improve their behavior by accumulating and updating experience at test time, yet existing evaluations often assume…

cs.IR202433 cited

TWIN V2: Scaling Ultra-Long User Behavior Sequence Modeling for Enhanced CTR Prediction at Kuaishou

Zihua Si, Lin Guan, ZhongXiang Sun +12

The significance of modeling long-term user interests for CTR prediction tasks in large-scale recommendation systems is progressively gaining attention among researchers and practi…

cs.IR2024

GradCraft: Elevating Multi-task Recommendations through Holistic Gradient Crafting

Yimeng Bai, Yang Zhang, Fuli Feng +4

Recommender systems require the simultaneous optimization of multiple objectives to accurately model user interests, necessitating the application of multi-task learning methods. H…

cs.IR2023

LabelCraft: Empowering Short Video Recommendations with Automated Label Crafting

Yimeng Bai, Yang Zhang, Jing Lu +5

Short video recommendations often face limitations due to the quality of user feedback, which may not accurately depict user interests. To tackle this challenge, a new task has eme…