Search Results for author: Zhirong Liu

Found 8 papers, 2 papers with code

Reversible Upper Confidence Bound Algorithm to Generate Diverse Optimized Candidates

no code implementations30 Dec 2021 Bin Chong, Yingguang Yang, Zi-Le Wang, Hang Xing, Zhirong Liu

Most algorithms for the multi-armed bandit problem in reinforcement learning aimed to maximize the expected reward, which are thus useful in searching the optimized candidate with the highest reward (function value) for diverse applications (e. g., AlphaGo).

Drug Discovery reinforcement-learning +1

Retrieval & Interaction Machine for Tabular Data Prediction

1 code implementation11 Aug 2021 Jiarui Qin, Weinan Zhang, Rong Su, Zhirong Liu, Weiwen Liu, Ruiming Tang, Xiuqiang He, Yong Yu

Prediction over tabular data is an essential task in many data science applications such as recommender systems, online advertising, medical treatment, etc.

Attribute Click-Through Rate Prediction +2

AutoFT: Automatic Fine-Tune for Parameters Transfer Learning in Click-Through Rate Prediction

no code implementations9 Jun 2021 Xiangli Yang, Qing Liu, Rong Su, Ruiming Tang, Zhirong Liu, Xiuqiang He

The field-wise transfer policy decides how the pre-trained embedding representations are frozen or fine-tuned based on the given instance from the target domain.

Click-Through Rate Prediction Recommendation Systems +1

Dual Graph enhanced Embedding Neural Network for CTR Prediction

no code implementations1 Jun 2021 Wei Guo, Rong Su, Renhao Tan, Huifeng Guo, Yingxue Zhang, Zhirong Liu, Ruiming Tang, Xiuqiang He

To solve these problems, we propose a novel module named Dual Graph enhanced Embedding, which is compatible with various CTR prediction models to alleviate these two problems.

Click-Through Rate Prediction Recommendation Systems

A Practical Incremental Method to Train Deep CTR Models

no code implementations4 Sep 2020 Yichao Wang, Huifeng Guo, Ruiming Tang, Zhirong Liu, Xiuqiang He

Deep learning models in recommender systems are usually trained in the batch mode, namely iteratively trained on a fixed-size window of training data.

Incremental Learning Recommendation Systems

Personalized Re-ranking for Improving Diversity in Live Recommender Systems

no code implementations14 Apr 2020 Yichao Wang, Xiangyu Zhang, Zhirong Liu, Zhenhua Dong, Xinhua Feng, Ruiming Tang, Xiuqiang He

To overcome such limitation, our re-ranking model proposes a personalized DPP to model the trade-off between accuracy and diversity for each individual user.

Recommendation Systems Re-Ranking

Uncovering Download Fraud Activities in Mobile App Markets

no code implementations5 Jul 2019 Yingtong Dou, Weijian Li, Zhirong Liu, Zhenhua Dong, Jiebo Luo, Philip S. Yu

To the best of our knowledge, this is the first work that investigates the download fraud problem in mobile App markets.

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