21 citations · 23 across the 6 of their papers we have counts for
6 papers · 1 filter
Discrete Conditional Diffusion for Reranking in Recommendation
Xiao Lin, Xiaokai Chen, Chenyang Wang +4
Reranking plays a crucial role in modern multi-stage recommender systems by rearranging the initial ranking list to model interplay between items. Considering the inherent challeng…
Measuring Item Global Residual Value for Fair Recommendation
Jiayin Wang, Weizhi Ma, Chumeng Jiang +4
In the era of information explosion, numerous items emerge every day, especially in feed scenarios. Due to the limited system display slots and user browsing attention, various rec…
Alleviating Matthew Effect of Offline Reinforcement Learning in Interactive Recommendation
Chongming Gao, Kexin Huang, Jiawei Chen +6
Offline reinforcement learning (RL), a technology that offline learns a policy from logged data without the need to interact with online environments, has become a favorable choice…
Tree based Progressive Regression Model for Watch-Time Prediction in Short-video Recommendation
Xiao Lin, Xiaokai Chen, Linfeng Song +3
An accurate prediction of watch time has been of vital importance to enhance user engagement in video recommender systems. To achieve this, there are four properties that a watch t…
Disentangled Causal Embedding With Contrastive Learning For Recommender System
Weiqi Zhao, Dian Tang, Xin Chen +5
Recommender systems usually rely on observed user interaction data to build personalized recommendation models, assuming that the observed data reflect user interest. However, user…
Divide and Conquer: Towards Better Embedding-based Retrieval for Recommender Systems From a Multi-task Perspective
Yuan Zhang, Xue Dong, Weijie Ding +3
Embedding-based retrieval (EBR) methods are widely used in modern recommender systems thanks to its simplicity and effectiveness. However, along the journey of deploying and iterat…