12 citations · 23 across the 5 of their papers we have counts for
6 papers
Retrieval & Interaction Machine for Tabular Data Prediction
Jiarui Qin, Weinan Zhang, Rong Su +5
Prediction over tabular data is an essential task in many data science applications such as recommender systems, online advertising, medical treatment, etc. Tabular data is structu…
AutoFT: Automatic Fine-Tune for Parameters Transfer Learning in Click-Through Rate Prediction
Xiangli Yang, Qing Liu, Rong Su +3
Recommender systems are often asked to serve multiple recommendation scenarios or domains. Fine-tuning a pre-trained CTR model from source domains and adapting it to a target domai…
Dual Graph enhanced Embedding Neural Network for CTR Prediction
Wei Guo, Rong Su, Renhao Tan +5
CTR prediction, which aims to estimate the probability that a user will click an item, plays a crucial role in online advertising and recommender system. Feature interaction modeli…
A Practical Incremental Method to Train Deep CTR Models
Yichao Wang, Huifeng Guo, Ruiming Tang +2
Deep learning models in recommender systems are usually trained in the batch mode, namely iteratively trained on a fixed-size window of training data. Such batch mode training of d…
Personalized Re-ranking for Improving Diversity in Live Recommender Systems
Yichao Wang, Xiangyu Zhang, Zhirong Liu +4
Users of industrial recommender systems are normally suggesteda list of items at one time. Ideally, such list-wise recommendationshould provide diverse and relevant options to the…
Uncovering Download Fraud Activities in Mobile App Markets
Yingtong Dou, Weijian Li, Zhirong Liu +3
Download fraud is a prevalent threat in mobile App markets, where fraudsters manipulate the number of downloads of Apps via various cheating approaches. Purchased fake downloads ca…