b'Ruiming Tang'

research

∙ 09/12/2023

HAMUR: Hyper Adapter for Multi-Domain Recommendation

Multi-Domain Recommendation (MDR) has gained significant attention in re...

0 Xiaopeng Li, et al. ∙

research

∙ 09/05/2023

Scenario-Aware Hierarchical Dynamic Network for Multi-Scenario Recommendation

Click-Through Rate (CTR) prediction is a fundamental technique in recomm...

0 Jingtong Gao, et al. ∙

research

∙ 08/22/2023

ReLLa: Retrieval-enhanced Large Language Models for Lifelong Sequential Behavior Comprehension in Recommendation

With large language models (LLMs) achieving remarkable breakthroughs in ...

0 Jianghao Lin, et al. ∙

research

∙ 08/19/2023

Time-aligned Exposure-enhanced Model for Click-Through Rate Prediction

Click-Through Rate (CTR) prediction, crucial in applications like recomm...

0 Hengyu Zhang, et al. ∙

research

∙ 08/15/2023

Dynamic Embedding Size Search with Minimum Regret for Streaming Recommender System

With the continuous increase of users and items, conventional recommende...

0 Bowei He, et al. ∙

research

∙ 08/14/2023

AutoAssign+: Automatic Shared Embedding Assignment in Streaming Recommendation

In the domain of streaming recommender systems, conventional methods for...

0 Ziru Liu, et al. ∙

research

∙ 08/03/2023

MAP: A Model-agnostic Pretraining Framework for Click-through Rate Prediction

With the widespread application of personalized online services, click-t...

0 Jianghao Lin, et al. ∙

research

∙ 06/26/2023

Contrastive Multi-view Framework for Customer Lifetime Value Prediction

Accurate customer lifetime value (LTV) prediction can help service provi...

0 Chuhan Wu, et al. ∙

research

∙ 06/15/2023

ReLoop2: Building Self-Adaptive Recommendation Models via Responsive Error Compensation Loop

Industrial recommender systems face the challenge of operating in non-st...

0 Jieming Zhu, et al. ∙

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...

0 Jianghao Lin, et al. ∙

research

∙ 06/05/2023

CTRL: Connect Tabular and Language Model for CTR Prediction

Traditional click-through rate (CTR) prediction models convert the tabul...

0 Xiangyang Li, et al. ∙

research

∙ 05/02/2023

Structure Aware Incremental Learning with Personalized Imitation Weights for Recommender Systems

Recommender systems now consume large-scale data and play a significant ...

0 Yuening Wang, et al. ∙

research

∙ 03/21/2023

Dynamically Expandable Graph Convolution for Streaming Recommendation

Personalized recommender systems have been widely studied and deployed t...

0 Bowei He, et al. ∙

research

∙ 02/22/2023

A Survey on User Behavior Modeling in Recommender Systems

User Behavior Modeling (UBM) plays a critical role in user interest lear...

0 Zhicheng He, et al. ∙

research

∙ 02/07/2023

Multi-Task Deep Recommender Systems: A Survey

Multi-task learning (MTL) aims at learning related tasks in a unified mo...

0 Yuhao Wang, et al. ∙

research

∙ 11/11/2022

Intent-aware Multi-source Contrastive Alignment for Tag-enhanced Recommendation

To offer accurate and diverse recommendation services, recent methods us...

0 Haolun Wu, et al. ∙

research

∙ 10/26/2022

Disentangling Past-Future Modeling in Sequential Recommendation via Dual Networks

Sequential recommendation (SR) plays an important role in personalized r...

0 Hengyu Zhang, et al. ∙

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...

0 Xiangyang Li, et al. ∙

research

∙ 09/05/2022

A Brief History of Recommender Systems

Soon after the invention of the Internet, the recommender system emerged...

1 Zhenhua Dong, et al. ∙

research

∙ 08/11/2022

Task Aligned Meta-learning based Augmented Graph for Cold-Start Recommendation

The cold-start problem is a long-standing challenge in recommender syste...

0 Yuxiang Shi, et al. ∙

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(...

0 Fuyuan Lyu, et al. ∙

research

∙ 08/03/2022

Coarse-to-Fine Knowledge-Enhanced Multi-Interest Learning Framework for Multi-Behavior Recommendation

Multi-types of behaviors (e.g., clicking, adding to cart, purchasing, et...

0 Chang Meng, et al. ∙

research

∙ 08/02/2022

Adapting Triplet Importance of Implicit Feedback for Personalized Recommendation

Implicit feedback is frequently used for developing personalized recomme...

0 Haolun Wu, et al. ∙

research

∙ 06/17/2022

A Graph-Enhanced Click Model for Web Search

To better exploit search logs and model users' behavior patterns, numero...

0 Jianghao Lin, et al. ∙

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...

0 Yankai Chen, et al. ∙

research

∙ 04/26/2022

Cross Pairwise Ranking for Unbiased Item Recommendation

Most recommender systems optimize the model on observed interaction data...

0 Qi Wan, et al. ∙

research

∙ 04/04/2022

Automated Machine Learning for Deep Recommender Systems: A Survey

Deep recommender systems (DRS) are critical for current commercial onlin...

0 Bo Chen, et al. ∙

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...

0 Yi Li, et al. ∙

research

∙ 01/27/2022

DropNAS: Grouped Operation Dropout for Differentiable Architecture Search

Neural architecture search (NAS) has shown encouraging results in automa...

0 Weijun Hong, et al. ∙

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...

0 Yankai Chen, et al. ∙

research

∙ 11/16/2021

On Effective Scheduling of Model-based Reinforcement Learning

Model-based reinforcement learning has attracted wide attention due to i...

0 Hang Lai, et al. ∙

research

∙ 11/16/2021

QA4PRF: A Question Answering based Framework for Pseudo Relevance Feedback

Pseudo relevance feedback (PRF) automatically performs query expansion b...

0 Handong Ma, et al. ∙

research

∙ 11/05/2021

AIM: Automatic Interaction Machine for Click-Through Rate Prediction

Feature embedding learning and feature interaction modeling are two cruc...

0 Chenxu Zhu, et al. ∙

research

∙ 10/25/2021

Content Filtering Enriched GNN Framework for News Recommendation

Learning accurate users and news representations is critical for news re...

0 Yong Gao, et al. ∙

research

∙ 10/18/2021

Context-aware Reranking with Utility Maximization for Recommendation

As a critical task for large-scale commercial recommender systems, reran...

0 Yunjia Xi, et al. ∙

research

∙ 09/28/2021

Extracting Attentive Social Temporal Excitation for Sequential Recommendation

In collaborative filtering, it is an important way to make full use of s...

0 Yunzhe Li, et al. ∙

research

∙ 08/11/2021

Retrieval Interaction Machine for Tabular Data Prediction

Prediction over tabular data is an essential task in many data science a...

0 Jiarui Qin, et al. ∙

research

∙ 06/25/2021

Balancing Accuracy and Fairness for Interactive Recommendation with Reinforcement Learning

Fairness in recommendation has attracted increasing attention due to bia...

0 Weiwen Liu, et al. ∙

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...

0 Xiangli Yang, et al. ∙

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 ...

0 Wei Guo, et al. ∙

research

∙ 04/21/2021

Deep Learning for Click-Through Rate Estimation

Click-through rate (CTR) estimation plays as a core function module in v...

0 Weinan Zhang, et al. ∙

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,...

0 Huifeng Guo, et al. ∙

research

∙ 04/13/2021

An Adversarial Imitation Click Model for Information Retrieval

Modern information retrieval systems, including web search, ads placemen...

0 Xinyi Dai, et al. ∙

research

∙ 01/13/2021

Probabilistic Metric Learning with Adaptive Margin for Top-K Recommendation

Personalized recommender systems are playing an increasingly important r...

0 Chen Ma, et al. ∙

research

∙ 12/16/2020

AutoDis: Automatic Discretization for Embedding Numerical Features in CTR Prediction

Learning sophisticated feature interactions is crucial for Click-Through...

0 Huifeng Guo, et al. ∙

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...

0 Xinyi Dai, et al. ∙

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...

0 Yichao Wang, et al. ∙

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 ...

0 Kelong Mao, et al. ∙

research

∙ 08/25/2020

GraphSAIL: Graph Structure Aware Incremental Learning for Recommender Systems

Given the convenience of collecting information through online services,...

39 Yishi Xu, et al. ∙

research

∙ 06/18/2020

Interactive Recommender System via Knowledge Graph-enhanced Reinforcement Learning

Interactive recommender system (IRS) has drawn huge attention because of...

0 Sijin Zhou, et al. ∙

Ruiming Tang

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