9 citations · 16 across the 6 of their papers we have counts for
6 papers
Discrete Semantic Tokenization for Deep CTR Prediction
Qijiong Liu, Hengchang Hu, Jiahao Wu +3
Incorporating item content information into click-through rate (CTR) prediction models remains a challenge, especially with the time and space constraints of industrial scenarios.…
Lightweight Modality Adaptation to Sequential Recommendation via Correlation Supervision
Hengchang Hu, Qijiong Liu, Chuang Li +1
In Sequential Recommenders (SR), encoding and utilizing modalities in an end-to-end manner is costly in terms of modality encoder sizes. Two-stage approaches can mitigate such conc…
Automatic Feature Fairness in Recommendation via Adversaries
Hengchang Hu, Yiming Cao, Zhankui He +2
Fairness is a widely discussed topic in recommender systems, but its practical implementation faces challenges in defining sensitive features while maintaining recommendation accur…
A Conversation is Worth A Thousand Recommendations: A Survey of Holistic Conversational Recommender Systems
Chuang Li, Hengchang Hu, Yan Zhang +2
Conversational recommender systems (CRS) generate recommendations through an interactive process. However, not all CRS approaches use human conversations as their source of interac…
Adaptive Multi-Modalities Fusion in Sequential Recommendation Systems
Hengchang Hu, Wei Guo, Yong Liu +1
In sequential recommendation, multi-modal information (e.g., text or image) can provide a more comprehensive view of an item's profile. The optimal stage (early or late) to fuse mo…
Do We Really Need Graph Neural Networks for Traffic Forecasting?
Xu Liu, Yuxuan Liang, Chao Huang +4
Spatio-temporal graph neural networks (STGNN) have become the most popular solution to traffic forecasting. While successful, they rely on the message passing scheme of GNNs to est…