CTR Prediction

Click-through rate (CTR) prediction aims to accurately estimate the probability of a user clicking on a recommended item or advertisement, a crucial task in online advertising and recommender systems. Current research emphasizes improving CTR prediction by addressing challenges like cold-start problems (lack of data for new items or users), modeling long-term user behavior and context, and efficiently handling high-dimensional data through techniques such as graph neural networks, deep learning architectures with attention mechanisms, and knowledge distillation. These advancements lead to more effective personalized recommendations and improved revenue generation for online platforms, impacting both the scientific understanding of user behavior and the practical performance of large-scale systems.

Papers

October 28, 2024

Enhancing CTR Prediction in Recommendation Domain with Search Query Representation
Yuening Wang, Man Chen, Yaochen Hu, Wei Guo, Yingxue Zhang, Huifeng Guo, Yong Liu, Mark Coates
Contrastive Learning Search Query Individual Representation Click Through Rate Prediction Personalized Recommendation Search Behavior CTR Prediction

October 8, 2024

Data Efficiency for Large Recommendation Models
Kshitij Jain, Jingru Xie, Kevin Regan, Cheng Chen, Jie Han, Steve Li, Zhuoshu Li, Todd Phillips, Myles Sussman, Matt Troup, Angel Yu, Jia Zhuo
CTR Prediction Data Efficiency Large Scale Recommender System Network Convergence

August 3, 2024

Scene-wise Adaptive Network for Dynamic Cold-start Scenes Optimization in CTR Prediction
Wenhao Li, Jie Zhou, Chuan Luo, Chao Tang, Kun Zhang, Shixiong Zhao
Similarity Learning CTR Prediction Cold Start Recommendation Scene Optimization Multi Scenario Recommendation

July 23, 2024

TWIN V2: Scaling Ultra-Long User Behavior Sequence Modeling for Enhanced CTR Prediction at Kuaishou
Zihua Si, Lin Guan, ZhongXiang Sun, Xiaoxue Zang, Jing Lu, Yiqun Hui, Xingchao Cao, Zeyu Yang, Yichen Zheng, Dewei Leng, Kai Zheng, Chenbin Zhang, Yanan Niu, Yang Song, Kun Gai
User Behavior CTR Prediction Octopus V2 Large Scale Recommendation System User Behavior Modeling

February 29, 2024

Improved Online Learning Algorithms for CTR Prediction in Ad Auctions
Zhe Feng, Christopher Liaw, Zixin Zhou
Online Service O$ Regret Online Learning Algorithm Ad Auction CTR Prediction Revenue Maximization

January 15, 2024

Deep Evolutional Instant Interest Network for CTR Prediction in Trigger-Induced Recommendation
Zhibo Xiao, Luwei Yang, Tao Zhang, Wen Jiang, Wei Ning, Yujiu Yang
User Interest CTR Prediction Interest Network

August 31, 2023

AntM$^{2}$C: A Large Scale Dataset For Multi-Scenario Multi-Modal CTR Prediction
Zhaoxin Huan, Ke Ding, Ang Li, Xiaolu Zhang, Xu Min, Yong He, Liang Zhang, Jun Zhou, Linjian Mo, Jinjie Gu, Zhongyi Liu, Wenliang Zhong, Guannan Zhang
Large Scale Dataset CTR Prediction

August 11, 2023

Deep Context Interest Network for Click-Through Rate Prediction
Xuyang Hou, Zhe Wang, Qi Liu, Tan Qu, Jia Cheng, Jun Lei
User Behavior Click Through Rate Prediction User Interest CTR Prediction Interest Network

July 25, 2023

Unleash the Power of Context: Enhancing Large-Scale Recommender Systems with Context-Based Prediction Models
Jan Hartman, Assaf Klein, Davorin Kopič, Natalia Silberstein
Context Information Real Power Context Aware Personalized Recommendation CTR Prediction Large Scale Recommender System

June 28, 2023

Confidence Ranking for CTR Prediction
Jian Zhu, Congcong Liu, Pei Wang, Xiwei Zhao, Zhangang Lin, Jingping Shao
Confidence Score CTR Prediction Ranking Function Direct Optimization

June 26, 2023

A Collaborative Transfer Learning Framework for Cross-domain Recommendation
Wei Zhang, Pengye Zhang, Bo Zhang, Xingxing Wang, Dong Wang
Transfer Learning Cross Domain Recommendation Domain Specific Feature CTR Prediction Transfer Learning Framework

April 10, 2023

FAN: Fatigue-Aware Network for Click-Through Rate Prediction in E-commerce Recommendation
Ming Li, Naiyin Liu, Xiaofeng Pan, Yang Huang, Ningning Li, Yingmin Su, Chengjun Mao, Bo Cao
Click Through Rate Prediction Fatigue Induced CTR Prediction Biased Feedback E Commerce Recommendation

January 30, 2023

Causality-based CTR Prediction using Graph Neural Networks
Panyu Zhai, Yanwu Yang, Chunjie Zhang
Graph Neural Network CTR Prediction Neural Causal Model

January 3, 2023

DynInt: Dynamic Interaction Modeling for Large-scale Click-Through Rate Prediction
YaChen Yan, Liubo Li
Click Through Rate Prediction Feature Interaction CTR Prediction Polynomial Network

November 21, 2022

Directed Acyclic Graph Factorization Machines for CTR Prediction via Knowledge Distillation
Zhen Tian, Ting Bai, Zibin Zhang, Zhiyuan Xu, Kangyi Lin, Ji-Rong Wen, Wayne Xin Zhao
Knowledge Distillation CTR Prediction dlADMM Algorithm

November 16, 2022

Deep Intention-Aware Network for Click-Through Rate Prediction
Yaxian Xia, Yi Cao, Sihao Hu, Tong Liu, Lingling Lu
Click Through Rate Prediction Intent Prediction User Intent CTR Prediction Trigger Design

October 25, 2022

MemoNet: Memorizing All Cross Features' Representations Efficiently via Multi-Hash Codebook Network for CTR Prediction
Pengtao Zhang, Junlin Zhang
Large Language Model Meaningful Representation CTR Prediction

October 9, 2022

SML:Enhance the Network Smoothness with Skip Meta Logit for CTR Prediction
Wenlong Deng, Lang Lang, Zhen Liu, Bin Liu
CTR Prediction Normalization Method

August 12, 2022

Joint Optimization of Ranking and Calibration with Contextualized Hybrid Model
Xiang-Rong Sheng, Jingyue Gao, Yueyao Cheng, Siran Yang, Shuguang Han, Hongbo Deng, Yuning Jiang, Jian Xu, Bo Zheng
Calibration Performance Partial Ranking Click Through Rate Prediction Joint Optimization Context Modeling CTR Prediction Click Model

June 28, 2022

Meta-Wrapper: Differentiable Wrapping Operator for User Interest Selection in CTR Prediction
Tianwei Cao, Qianqian Xu, Zhiyong Yang, Qingming Huang
Recommender System User Interest CTR Prediction Differential Operator New Wrapper Method

CTR Prediction

Papers

Enhancing CTR Prediction in Recommendation Domain with Search Query Representation

Data Efficiency for Large Recommendation Models

Scene-wise Adaptive Network for Dynamic Cold-start Scenes Optimization in CTR Prediction

TWIN V2: Scaling Ultra-Long User Behavior Sequence Modeling for Enhanced CTR Prediction at Kuaishou

Improved Online Learning Algorithms for CTR Prediction in Ad Auctions

Deep Evolutional Instant Interest Network for CTR Prediction in Trigger-Induced Recommendation

AntM$^{2}$C: A Large Scale Dataset For Multi-Scenario Multi-Modal CTR Prediction

Deep Context Interest Network for Click-Through Rate Prediction

Unleash the Power of Context: Enhancing Large-Scale Recommender Systems with Context-Based Prediction Models

Confidence Ranking for CTR Prediction

A Collaborative Transfer Learning Framework for Cross-domain Recommendation

FAN: Fatigue-Aware Network for Click-Through Rate Prediction in E-commerce Recommendation

Causality-based CTR Prediction using Graph Neural Networks

DynInt: Dynamic Interaction Modeling for Large-scale Click-Through Rate Prediction

Directed Acyclic Graph Factorization Machines for CTR Prediction via Knowledge Distillation

Deep Intention-Aware Network for Click-Through Rate Prediction

MemoNet: Memorizing All Cross Features' Representations Efficiently via Multi-Hash Codebook Network for CTR Prediction

SML:Enhance the Network Smoothness with Skip Meta Logit for CTR Prediction

Joint Optimization of Ranking and Calibration with Contextualized Hybrid Model

Meta-Wrapper: Differentiable Wrapping Operator for User Interest Selection in CTR Prediction