70 citations · 76 across the 5 of their papers we have counts for
6 papers
GREAT: Guiding Query Generation with a Trie for Recommending Related Search about Video at Kuaishou
Ninglu Shao, Jinshan Wang, Chenxu Wang +3
Currently, short video platforms have become the primary place for individuals to share experiences and obtain information. To better meet users' needs for acquiring information wh…
Multi-Modal Hypergraph Enhanced LLM Learning for Recommendation
Xu Guo, Tong Zhang, Yuanzhi Wang +6
The burgeoning presence of Large Language Models (LLM) is propelling the development of personalized recommender systems. Most existing LLM-based methods fail to sufficiently explo…
Lower-Left Partial AUC: An Effective and Efficient Optimization Metric for Recommendation
Wentao Shi, Chenxu Wang, Fuli Feng +4
Optimization metrics are crucial for building recommendation systems at scale. However, an effective and efficient metric for practical use remains elusive. While Top-K ranking met…
Prediction then Correction: An Abductive Prediction Correction Method for Sequential Recommendation
Yulong Huang, Yang Zhang, Qifan Wang +2
Sequential recommender models typically generate predictions in a single step during testing, without considering additional prediction correction to enhance performance as humans…
Rethinking Missing Data: Aleatoric Uncertainty-Aware Recommendation
Chenxu Wang, Fuli Feng, Yang Zhang +3
Historical interactions are the default choice for recommender model training, which typically exhibit high sparsity, i.e., most user-item pairs are unobserved missing data. A stan…
How to Retrain Recommender System? A Sequential Meta-Learning Method
Yang Zhang, Fuli Feng, Chenxu Wang +4
Practical recommender systems need be periodically retrained to refresh the model with new interaction data. To pursue high model fidelity, it is usually desirable to retrain the m…