89 citations · 136 across the 13 of their papers we have counts for
13 papers
Exploring Scaling Laws of CTR Model for Online Performance Improvement
Weijiang Lai, Beihong Jin, Jiongyan Zhang +5
CTR models play a vital role in improving user experience and boosting business revenue in many online personalized services. However, current CTR models generally encounter bottle…
Efficient Transfer Learning Framework for Cross-Domain Click-Through Rate Prediction
Qi Liu, Xingyuan Tang, Jianqiang Huang +9
Natural content and advertisement coexist in industrial recommendation systems but differ in data distribution. Concretely, traffic related to the advertisement is considerably spa…
AT4CTR: Auxiliary Match Tasks for Enhancing Click-Through Rate Prediction
Qi Liu, Xuyang Hou, Defu Lian +4
Click-through rate (CTR) prediction is a vital task in industrial recommendation systems. Most existing methods focus on the network architecture design of the CTR model for better…
Deep Group Interest Modeling of Full Lifelong User Behaviors for CTR Prediction
Qi Liu, Xuyang Hou, Haoran Jin +6
Extracting users' interests from their lifelong behavior sequence is crucial for predicting Click-Through Rate (CTR). Most current methods employ a two-stage process for efficiency…
STGIN: Spatial-Temporal Graph Interaction Network for Large-scale POI Recommendation
Shaohua Liu, Yu Qi, Gen Li +4
In Location-Based Services, Point-Of-Interest(POI) recommendation plays a crucial role in both user experience and business opportunities. Graph neural networks have been proven ef…
SpikingNeRF: Making Bio-inspired Neural Networks See through the Real World
Xingting Yao, Qinghao Hu, Fei Zhou +5
In this paper, we propose SpikingNeRF, which aligns the temporal dimension of spiking neural networks (SNNs) with the radiance rays, to seamlessly accommodate SNNs to the reconstru…
Deep Context Interest Network for Click-Through Rate Prediction
Xuyang Hou, Zhe Wang, Qi Liu +3
Click-Through Rate (CTR) prediction, estimating the probability of a user clicking on an item, is essential in industrial applications, such as online advertising. Many works focus…
DisenPOI: Disentangling Sequential and Geographical Influence for Point-of-Interest Recommendation
Yifang Qin, Yifan Wang, Fang Sun +6
Point-of-Interest (POI) recommendation plays a vital role in various location-aware services. It has been observed that POI recommendation is driven by both sequential and geograph…
Hybrid CNN Based Attention with Category Prior for User Image Behavior Modeling
Xin Chen, Qingtao Tang, Ke Hu +4
User historical behaviors are proved useful for Click Through Rate (CTR) prediction in online advertising system. In Meituan, one of the largest e-commerce platform in China, an it…
Continual Learning for CTR Prediction: A Hybrid Approach
Ke Hu, Yi Qi, Jianqiang Huang +2
Click-through rate(CTR) prediction is a core task in cost-per-click(CPC) advertising systems and has been studied extensively by machine learning practitioners. While many existing…
AutoHEnsGNN: Winning Solution to AutoGraph Challenge for KDD Cup 2020
Jin Xu, Mingjian Chen, Jianqiang Huang +5
Graph Neural Networks (GNNs) have become increasingly popular and achieved impressive results in many graph-based applications. However, extensive manual work and domain knowledge…
Deep Position-wise Interaction Network for CTR Prediction
Jianqiang Huang, Ke Hu, Qingtao Tang +4
Click-through rate (CTR) prediction plays an important role in online advertising and recommender systems. In practice, the training of CTR models depends on click data which is in…
A Concept Knowledge-Driven Keywords Retrieval Framework for Sponsored Search
Yijiang Lian, Yubo Liu, Zhicong Ye +5
In sponsored search, retrieving synonymous keywords for exact match type is important for accurately targeted advertising. Data-driven deep learning-based method has been proposed…