53 citations · 258 across the 27 of their papers we have counts for
5 papers · 2 filters
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…
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…
UniSAR: Modeling User Transition Behaviors between Search and Recommendation
Teng Shi, Zihua Si, Jun Xu +6
Nowadays, many platforms provide users with both search and recommendation services as important tools for accessing information. The phenomenon has led to a correlation between us…
To Search or to Recommend: Predicting Open-App Motivation with Neural Hawkes Process
Zhongxiang Sun, Zihua Si, Xiao Zhang +4
Incorporating Search and Recommendation (S&R) services within a singular application is prevalent in online platforms, leading to a new task termed open-app motivation prediction,…
Large Language Models Enhanced Collaborative Filtering
Zhongxiang Sun, Zihua Si, Xiaoxue Zang +4
Recent advancements in Large Language Models (LLMs) have attracted considerable interest among researchers to leverage these models to enhance Recommender Systems (RSs). Existing w…