research
∙
09/12/2023
HAMUR: Hyper Adapter for Multi-Domain Recommendation
Multi-Domain Recommendation (MDR) has gained significant attention in re...
share
research
∙
09/05/2023
Scenario-Aware Hierarchical Dynamic Network for Multi-Scenario Recommendation
Click-Through Rate (CTR) prediction is a fundamental technique in recomm...
research
∙
08/19/2023
Time-aligned Exposure-enhanced Model for Click-Through Rate Prediction
Click-Through Rate (CTR) prediction, crucial in applications like recomm...
research
∙
08/14/2023
AutoAssign+: Automatic Shared Embedding Assignment in Streaming Recommendation
In the domain of streaming recommender systems, conventional methods for...
research
∙
06/09/2023
How Can Recommender Systems Benefit from Large Language Models: A Survey
Recommender systems (RS) play important roles to match users' informatio...
research
∙
02/07/2023
Multi-Task Deep Recommender Systems: A Survey
Multi-task learning (MTL) aims at learning related tasks in a unified mo...
research
∙
10/26/2022
Disentangling Past-Future Modeling in Sequential Recommendation via Dual Networks
Sequential recommendation (SR) plays an important role in personalized r...
research
∙
10/18/2022
IntTower: the Next Generation of Two-Tower Model for Pre-Ranking System
Scoring a large number of candidates precisely in several milliseconds i...
research
∙
08/09/2022
OptEmbed: Learning Optimal Embedding Table for Click-through Rate Prediction
Learning embedding table plays a fundamental role in Click-through rate(...
research
∙
06/05/2022
Learning Binarized Graph Representations with Multi-faceted Quantization Reinforcement for Top-K Recommendation
Learning vectorized embeddings is at the core of various recommender sys...
research
∙
04/04/2022
Automated Machine Learning for Deep Recommender Systems: A Survey
Deep recommender systems (DRS) are critical for current commercial onlin...
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
∙
10/25/2021
Content Filtering Enriched GNN Framework for News Recommendation
Learning accurate users and news representations is critical for news re...
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
∙
04/17/2021
ScaleFreeCTR: MixCache-based Distributed Training System for CTR Models with Huge Embedding Table
Because of the superior feature representation ability of deep learning,...
research
∙
12/16/2020
AutoDis: Automatic Discretization for Embedding Numerical Features in CTR Prediction
Learning sophisticated feature interactions is crucial for Click-Through...
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
∙
08/25/2020
GraphSAIL: Graph Structure Aware Incremental Learning for Recommender Systems
Given the convenience of collecting information through online services,...
research
∙
04/25/2020
Inter-sequence Enhanced Framework for Personalized Sequential Recommendation
Modeling the sequential correlation of users' historical interactions is...
research
∙
01/01/2020
Multi-Graph Convolution Collaborative Filtering
Personalized recommendation is ubiquitous, playing an important role in ...
research
∙
04/09/2019
Feature Generation by Convolutional Neural Network for Click-Through Rate Prediction
Click-Through Rate prediction is an important task in recommender system...
research
∙
10/29/2018
Deep Reinforcement Learning based Recommendation with Explicit User-Item Interactions Modeling
Recommendation is crucial in both academia and industry, and various tec...
research
∙
07/16/2018
An Adjustable Heat Conduction based KNN Approach for Session-based Recommendation
The KNN approach, which is widely used in recommender systems because of...
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
∙
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
∙
03/13/2017