6 citations · 11 across the 4 of their papers we have counts for
6 papers
Click-through Rate Prediction with Auto-Quantized Contrastive Learning
Yujie Pan, Jiangchao Yao, Bo Han +3
Click-through rate (CTR) prediction becomes indispensable in ubiquitous web recommendation applications. Nevertheless, the current methods are struggling under the cold-start scena…
Dynamic Sequential Graph Learning for Click-Through Rate Prediction
Yunfei Chu, Xiaofu Chang, Kunyang Jia +2
Click-through rate prediction plays an important role in the field of recommender system and many other applications. Existing methods mainly extract user interests from user histo…
MC-SF: Slow-Fast Learning for Mobile-Cloud Collaborative Recommendation
Zeyuan Chen, Jiangchao Yao, Feng Wang +4
With the hardware development of mobile devices, it is possible to build the recommendation models on the mobile side to utilize the fine-grained features and the real-time feedbac…
Device-Cloud Collaborative Learning for Recommendation
Jiangchao Yao, Feng Wang, KunYang Jia +3
With the rapid development of storage and computing power on mobile devices, it becomes critical and popular to deploy models on devices to save onerous communication latencies and…
Inductive Granger Causal Modeling for Multivariate Time Series
Yunfei Chu, Xiaowei Wang, Jianxin Ma +3
Granger causal modeling is an emerging topic that can uncover Granger causal relationship behind multivariate time series data. In many real-world systems, it is common to encounte…
Bayes EMbedding (BEM): Refining Representation by Integrating Knowledge Graphs and Behavior-specific Networks
Yuting Ye, Xuwu Wang, Jiangchao Yao +4
Low-dimensional embeddings of knowledge graphs and behavior graphs have proved remarkably powerful in varieties of tasks, from predicting unobserved edges between entities to conte…