research
∙
06/23/2023
OptMSM: Optimizing Multi-Scenario Modeling for Click-Through Rate Prediction
A large-scale industrial recommendation platform typically consists of m...
share
research
∙
06/01/2023
Explicit Feature Interaction-aware Uplift Network for Online Marketing
As a key component in online marketing, uplift modeling aims to accurate...
research
∙
04/25/2023
Curriculum Modeling the Dependence among Targets with Multi-task Learning for Financial Marketing
Multi-task learning for various real-world applications usually involves...
research
∙
02/07/2023
Self-Sampling Training and Evaluation for the Accuracy-Bias Tradeoff in Recommendation
Research on debiased recommendation has shown promising results. However...
research
∙
02/04/2023
Feature Representation Learning for Click-through Rate Prediction: A Review and New Perspectives
Representation learning has been a critical topic in machine learning. I...
research
∙
01/26/2023
Optimizing Feature Set for Click-Through Rate Prediction
Click-through prediction (CTR) models transform features into latent vec...
research
∙
07/06/2022
DIWIFT: Discovering Instance-wise Influential Features for Tabular Data
Tabular data is one of the most common data storage formats in business ...
research
∙
06/17/2022
A Graph-Enhanced Click Model for Web Search
To better exploit search logs and model users' behavior patterns, numero...
research
∙
03/23/2022
PEAR: Personalized Re-ranking with Contextualized Transformer for Recommendation
The goal of recommender systems is to provide ordered item lists to user...
research
∙
12/03/2021
Towards Low-loss 1-bit Quantization of User-item Representations for Top-K Recommendation
Due to the promising advantages in space compression and inference accel...
research
∙
11/16/2021
QA4PRF: A Question Answering based Framework for Pseudo Relevance Feedback
Pseudo relevance feedback (PRF) automatically performs query expansion b...
research
∙
11/05/2021
AIM: Automatic Interaction Machine for Click-Through Rate Prediction
Feature embedding learning and feature interaction modeling are two cruc...
research
∙
10/28/2021
Cross-Batch Negative Sampling for Training Two-Tower Recommenders
The two-tower architecture has been widely applied for learning item and...
research
∙
10/28/2021
UltraGCN: Ultra Simplification of Graph Convolutional Networks for Recommendation
With the recent success of graph convolutional networks (GCNs), they hav...
research
∙
10/25/2021
Content Filtering Enriched GNN Framework for News Recommendation
Learning accurate users and news representations is critical for news re...
research
∙
10/18/2021
Context-aware Reranking with Utility Maximization for Recommendation
As a critical task for large-scale commercial recommender systems, reran...
research
∙
09/26/2021
Why Do We Click: Visual Impression-aware News Recommendation
There is a soaring interest in the news recommendation research scenario...
research
∙
09/26/2021
SimpleX: A Simple and Strong Baseline for Collaborative Filtering
Collaborative filtering (CF) is a widely studied research topic in recom...
research
∙
08/11/2021
Retrieval Interaction Machine for Tabular Data Prediction
Prediction over tabular data is an essential task in many data science a...
research
∙
06/09/2021
AutoFT: Automatic Fine-Tune for Parameters Transfer Learning in Click-Through Rate Prediction
Recommender systems are often asked to serve multiple recommendation sce...
research
∙
06/01/2021
Dual Graph enhanced Embedding Neural Network for CTR Prediction
CTR prediction, which aims to estimate the probability that a user will ...
research
∙
04/21/2021
Deep Learning for Click-Through Rate Estimation
Click-through rate (CTR) estimation plays as a core function module in v...
research
∙
04/17/2021
ScaleFreeCTR: MixCache-based Distributed Training System for CTR Models with Huge Embedding Table
Because of the superior feature representation ability of deep learning,...
research
∙
04/13/2021
An Adversarial Imitation Click Model for Information Retrieval
Modern information retrieval systems, including web search, ads placemen...
research
∙
04/01/2021
Modeling High-order Interactions across Multi-interests for Micro-video Recommendation
Personalized recommendation system has become pervasive in various video...
research
∙
12/16/2020
AutoDis: Automatic Discretization for Embedding Numerical Features in CTR Prediction
Learning sophisticated feature interactions is crucial for Click-Through...
research
∙
11/08/2020
Ensembled CTR Prediction via Knowledge Distillation
Recently, deep learning-based models have been widely studied for click-...
research
∙
11/01/2020
U-rank: Utility-oriented Learning to Rank with Implicit Feedback
Learning to rank with implicit feedback is one of the most important tas...
research
∙
09/12/2020
FuxiCTR: An Open Benchmark for Click-Through Rate Prediction
In many applications, such as recommender systems, online advertising, a...
research
∙
09/04/2020
A Practical Incremental Method to Train Deep CTR Models
Deep learning models in recommender systems are usually trained in the b...
research
∙
08/26/2020
Item Tagging for Information Retrieval: A Tripartite Graph Neural Network based Approach
Tagging has been recognized as a successful practice to boost relevance ...
research
∙
08/19/2020
Regularized Two-Branch Proposal Networks for Weakly-Supervised Moment Retrieval in Videos
Video moment retrieval aims to localize the target moment in an video ac...
research
∙
06/18/2020
Interactive Recommender System via Knowledge Graph-enhanced Reinforcement Learning
Interactive recommender system (IRS) has drawn huge attention because of...
research
∙
04/25/2020
Inter-sequence Enhanced Framework for Personalized Sequential Recommendation
Modeling the sequential correlation of users' historical interactions is...
research
∙
04/14/2020
Personalized Re-ranking for Improving Diversity in Live Recommender Systems
Users of industrial recommender systems are normally suggesteda list of ...
research
∙
03/25/2020
AutoFIS: Automatic Feature Interaction Selection in Factorization Models for Click-Through Rate Prediction
Learning effective feature interactions is crucial for click-through rat...
research
∙
01/22/2020
MetaSelector: Meta-Learning for Recommendation with User-Level Adaptive Model Selection
Recommender systems often face heterogeneous datasets containing highly ...
research
∙
01/01/2020
Multi-Graph Convolution Collaborative Filtering
Personalized recommendation is ubiquitous, playing an important role in ...
research
∙
12/03/2019
Less Is Better: Unweighted Data Subsampling via Influence Function
In the time of Big Data, training complex models on large-scale data set...
research
∙
05/24/2019
Learning Cross-Domain Representation with Multi-Graph Neural Network
Learning effective embedding has been proved to be useful in many real-w...
research
∙
07/01/2018
Product-based Neural Networks for User Response Prediction over Multi-field Categorical Data
User response prediction is a crucial component for personalized informa...
research
∙
04/12/2018
DeepFM: An End-to-End Wide & Deep Learning Framework for CTR Prediction
Learning sophisticated feature interactions behind user behaviors is cri...
research
∙
03/09/2018
Collaborative Filtering with Graph-based Implicit Feedback
Introducing consumed items as users' implicit feedback in matrix factori...
research
∙
02/23/2018
Novel Approaches to Accelerating the Convergence Rate of Markov Decision Process for Search Result Diversification
Recently, some studies have utilized the Markov Decision Process for div...
research
∙
02/22/2018
Federated Meta-Learning for Recommendation
Recommender systems have been widely studied from the machine learning p...
research
∙
03/13/2017