542 citations · 1.5k across the 65 of their papers we have counts for
23 papers · 1 filter
AIM: Automatic Interaction Machine for Click-Through Rate Prediction
Chenxu Zhu, Bo Chen, Weinan Zhang +5
Feature embedding learning and feature interaction modeling are two crucial components of deep models for Click-Through Rate (CTR) prediction. Most existing deep CTR models suffer…
Towards Low-loss 1-bit Quantization of User-item Representations for Top-K Recommendation
Yankai Chen, Yifei Zhang, Yingxue Zhang +5
Due to the promising advantages in space compression and inference acceleration, quantized representation learning for recommender systems has become an emerging research direction…
QA4PRF: A Question Answering based Framework for Pseudo Relevance Feedback
Handong Ma, Jiawei Hou, Chenxu Zhu +6
Pseudo relevance feedback (PRF) automatically performs query expansion based on top-retrieved documents to better represent the user's information need so as to improve the search…
Transient Stability of Low-Inertia Power Systems with Inverter-Based Generation
Changjun He, Xiuqiang He, Hua Geng +2
This study examines the transient stability of low-inertia power systems with inverter-based generation (IBG) and proposes a sufficient stability criterion. In low-inertia grids, t…
MISS: Multi-Interest Self-Supervised Learning Framework for Click-Through Rate Prediction
Wei Guo, Can Zhang, Zhicheng He +6
CTR prediction is essential for modern recommender systems. Ranging from early factorization machines to deep learning based models in recent years, existing CTR methods focus on c…
Cross-Batch Negative Sampling for Training Two-Tower Recommenders
Jinpeng Wang, Jieming Zhu, Xiuqiang He
The two-tower architecture has been widely applied for learning item and user representations, which is important for large-scale recommender systems. Many two-tower models are tra…
Content Filtering Enriched GNN Framework for News Recommendation
Yong Gao, Huifeng Guo, Dandan Lin +3
Learning accurate users and news representations is critical for news recommendation. Despite great progress, existing methods seem to have a strong bias towards content representa…
UltraGCN: Ultra Simplification of Graph Convolutional Networks for Recommendation
Kelong Mao, Jieming Zhu, Xi Xiao +3
With the recent success of graph convolutional networks (GCNs), they have been widely applied for recommendation, and achieved impressive performance gains. The core of GCNs lies i…
Context-aware Reranking with Utility Maximization for Recommendation
Yunjia Xi, Weiwen Liu, Xinyi Dai +5
As a critical task for large-scale commercial recommender systems, reranking has shown the potential of improving recommendation results by uncovering mutual influence among items.…
Why Do We Click: Visual Impression-aware News Recommendation
Jiahao Xun, Shengyu Zhang, Zhou Zhao +7
There is a soaring interest in the news recommendation research scenario due to the information overload. To accurately capture users' interests, we propose to model multi-modal fe…
Mode Clustering Based Dynamic Equivalent Modeling of Wind Farm for Small-Signal Stability Analysis
Xiuqiang He, Hua Geng, Geng Yang
Dynamic equivalent models (DEMs) are necessities for the small-signal stability (SSS) analysis of the power system with large-scale wind farms (WFs). This paper proposes a mode clu…
Top-N Recommendation with Counterfactual User Preference Simulation
Mengyue Yang, Quanyu Dai, Zhenhua Dong +3
Top-N recommendation, which aims to learn user ranking-based preference, has long been a fundamental problem in a wide range of applications. Traditional models usually motivate th…
SimpleX: A Simple and Strong Baseline for Collaborative Filtering
Kelong Mao, Jieming Zhu, Jinpeng Wang +4
Collaborative filtering (CF) is a widely studied research topic in recommender systems. The learning of a CF model generally depends on three major components, namely interaction e…
Modeling Relevance Ranking under the Pre-training and Fine-tuning Paradigm
Lin Bo, Liang Pang, Gang Wang +3
Recently, pre-trained language models such as BERT have been applied to document ranking for information retrieval, which first pre-train a general language model on an unlabeled l…
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…
Memorize, Factorize, or be Naïve: Learning Optimal Feature Interaction Methods for CTR Prediction
Fuyuan Lyu, Xing Tang, Huifeng Guo +4
Click-through rate prediction is one of the core tasks in commercial recommender systems. It aims to predict the probability of a user clicking a particular item given user and ite…
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…
ScaleFreeCTR: MixCache-based Distributed Training System for CTR Models with Huge Embedding Table
Huifeng Guo, Wei Guo, Yong Gao +3
Because of the superior feature representation ability of deep learning, various deep Click-Through Rate (CTR) models are deployed in the commercial systems by industrial companies…
Modeling High-order Interactions across Multi-interests for Micro-video Recommendation
Dong Yao, Shengyu Zhang, Zhou Zhao +4
Personalized recommendation system has become pervasive in various video platform. Many effective methods have been proposed, but most of them didn't capture the user's multi-level…
Deep Learning for Click-Through Rate Estimation
Weinan Zhang, Jiarui Qin, Wei Guo +2
Click-through rate (CTR) estimation plays as a core function module in various personalized online services, including online advertising, recommender systems, and web search etc.…
An Adversarial Imitation Click Model for Information Retrieval
Xinyi Dai, Jianghao Lin, Weinan Zhang +7
Modern information retrieval systems, including web search, ads placement, and recommender systems, typically rely on learning from user feedback. Click models, which study how use…
Transient Stability of Hybrid Power Systems Dominated by Different Types of Grid-Forming Devices
Xiuqiang He, Hua Geng
This paper investigates the transient stability of power systems co-dominated by different types of grid-forming (GFM) devices. Synchronous generators (SGs and VSGs) and droop-cont…