48 citations · 106 across the 11 of their papers we have counts for
11 papers
Feature Staleness Aware Incremental Learning for CTR Prediction
Zhikai Wang, Yanyan Shen, Zibin Zhang +1
Click-through Rate (CTR) prediction in real-world recommender systems often deals with billions of user interactions every day. To improve the training efficiency, it is common to…
Towards Fine-Grained Explainability for Heterogeneous Graph Neural Network
Tong Li, Jiale Deng, Yanyan Shen +3
Heterogeneous graph neural networks (HGNs) are prominent approaches to node classification tasks on heterogeneous graphs. Despite the superior performance, insights about the predi…
SSIN: Self-Supervised Learning for Rainfall Spatial Interpolation
Jia Li, Yanyan Shen, Lei Chen +1
The acquisition of accurate rainfall distribution in space is an important task in hydrological analysis and natural disaster pre-warning. However, it is impossible to install rain…
DoubleAdapt: A Meta-learning Approach to Incremental Learning for Stock Trend Forecasting
Lifan Zhao, Shuming Kong, Yanyan Shen
Stock trend forecasting is a fundamental task of quantitative investment where precise predictions of price trends are indispensable. As an online service, stock data continuously…
RESUS: Warm-Up Cold Users via Meta-Learning Residual User Preferences in CTR Prediction
Yanyan Shen, Lifan Zhao, Weiyu Cheng +3
Click-Through Rate (CTR) prediction on cold users is a challenging task in recommender systems. Recent researches have resorted to meta-learning to tackle the cold-user challenge,…
Differentiable Neural Input Search for Recommender Systems
Weiyu Cheng, Yanyan Shen, Linpeng Huang
Latent factor models are the driving forces of the state-of-the-art recommender systems, with an important insight of vectorizing raw input features into dense embeddings. The dime…
Adaptive Factorization Network: Learning Adaptive-Order Feature Interactions
Weiyu Cheng, Yanyan Shen, Linpeng Huang
Various factorization-based methods have been proposed to leverage second-order, or higher-order cross features for boosting the performance of predictive models. They generally en…
Revisiting Flow Information for Traffic Prediction
Xian Zhou, Yanyan Shen, Linpeng Huang
Traffic prediction is a fundamental task in many real applications, which aims to predict the future traffic volume in any region of a city. In essence, traffic volume in a region…
STEP : A Distributed Multi-threading Framework Towards Efficient Data Analytics
Yijie Mei, Yanyan Shen, Yanmin Zhu +1
Various general-purpose distributed systems have been proposed to cope with high-diversity applications in the pipeline of Big Data analytics. Most of them provide simple yet effec…
Explaining Latent Factor Models for Recommendation with Influence Functions
Weiyu Cheng, Yanyan Shen, Yanmin Zhu +1
Latent factor models (LFMs) such as matrix factorization achieve the state-of-the-art performance among various Collaborative Filtering (CF) approaches for recommendation. Despite…
Modeling Conceptual Characteristics of Virtual Machines for CPU Utilization Prediction
Shengwei Chen, Yanyan Shen, Yanmin Zhu
Cloud services have grown rapidly in recent years, which provide high flexibility for cloud users to fulfill their computing requirements on demand. To wisely allocate computing re…