542 citations · 1.5k across the 65 of their papers we have counts for
15 papers · 1 filter
An Embedding Learning Framework for Numerical Features in CTR Prediction
Huifeng Guo, Bo Chen, Ruiming Tang +3
Click-Through Rate (CTR) prediction is critical for industrial recommender systems, where most deep CTR models follow an Embedding \& Feature Interaction paradigm. However, the maj…
Synchronization Instability of Inverter-Based Generation During Asymmetrical Grid Faults
Xiuqiang He, Changjun He, Sisi Pan +2
The transient stability of traditional power systems is concerned with the ability of generators to stay synchronized with the positive-sequence voltage of the network, whether for…
Ensemble Knowledge Distillation for CTR Prediction
Jieming Zhu, Jinyang Liu, Weiqi Li +4
Recently, deep learning-based models have been widely studied for click-through rate (CTR) prediction and lead to improved prediction accuracy in many industrial applications. Howe…
U-rank: Utility-oriented Learning to Rank with Implicit Feedback
Xinyi Dai, Jiawei Hou, Qing Liu +6
Learning to rank with implicit feedback is one of the most important tasks in many real-world information systems where the objective is some specific utility, e.g., clicks and rev…
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…
Open Benchmarking for Click-Through Rate Prediction
Jieming Zhu, Jinyang Liu, Shuai Yang +2
Click-through rate (CTR) prediction is a critical task for many applications, as its accuracy has a direct impact on user experience and platform revenue. In recent years, CTR pred…
Item Tagging for Information Retrieval: A Tripartite Graph Neural Network based Approach
Kelong Mao, Xi Xiao, Jieming Zhu +3
Tagging has been recognized as a successful practice to boost relevance matching for information retrieval (IR), especially when items lack rich textual descriptions. A lot of rese…
Regularized Two-Branch Proposal Networks for Weakly-Supervised Moment Retrieval in Videos
Zhu Zhang, Zhijie Lin, Zhou Zhao +2
Video moment retrieval aims to localize the target moment in an video according to the given sentence. The weak-supervised setting only provides the video-level sentence annotation…
Interactive Recommender System via Knowledge Graph-enhanced Reinforcement Learning
Sijin Zhou, Xinyi Dai, Haokun Chen +5
Interactive recommender system (IRS) has drawn huge attention because of its flexible recommendation strategy and the consideration of optimal long-term user experiences. To deal w…
Synchronization Stability Analysis and Enhancement of Grid-Tied Multi-Converter Systems
Xiuqiang He, Hua Geng
Synchronization instability of grid-tied converters during grid faults is a serious concern. For phase-locked loop (PLL) synchronized converters, synchronization stability during g…
Transient Stability of Power Systems Integrated With Inverter-Based Generation
Xiuqiang He, Hua Geng
The fundamental mechanism of transient stability of the conventional power system can be physically understood with the rotor swing dynamics of synchronous generators (SGs). The ba…
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…
AutoFIS: Automatic Feature Interaction Selection in Factorization Models for Click-Through Rate Prediction
Bin Liu, Chenxu Zhu, Guilin Li +6
Learning feature interactions is crucial for click-through rate (CTR) prediction in recommender systems. In most existing deep learning models, feature interactions are either manu…
MetaSelector: Meta-Learning for Recommendation with User-Level Adaptive Model Selection
Mi Luo, Fei Chen, Pengxiang Cheng +4
Recommender systems often face heterogeneous datasets containing highly personalized historical data of users, where no single model could give the best recommendation for every us…
Multi-Graph Convolution Collaborative Filtering
Jianing Sun, Yingxue Zhang, Chen Ma +4
Personalized recommendation is ubiquitous, playing an important role in many online services. Substantial research has been dedicated to learning vector representations of users an…