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09/12/2023
HAMUR: Hyper Adapter for Multi-Domain Recommendation
Multi-Domain Recommendation (MDR) has gained significant attention in re...
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09/05/2023
Scenario-Aware Hierarchical Dynamic Network for Multi-Scenario Recommendation
Click-Through Rate (CTR) prediction is a fundamental technique in recomm...
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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 ...
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08/19/2023
Time-aligned Exposure-enhanced Model for Click-Through Rate Prediction
Click-Through Rate (CTR) prediction, crucial in applications like recomm...
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08/15/2023
Dynamic Embedding Size Search with Minimum Regret for Streaming Recommender System
With the continuous increase of users and items, conventional recommende...
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08/14/2023
AutoAssign+: Automatic Shared Embedding Assignment in Streaming Recommendation
In the domain of streaming recommender systems, conventional methods for...
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08/03/2023
MAP: A Model-agnostic Pretraining Framework for Click-through Rate Prediction
With the widespread application of personalized online services, click-t...
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06/26/2023
Contrastive Multi-view Framework for Customer Lifetime Value Prediction
Accurate customer lifetime value (LTV) prediction can help service provi...
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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...
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06/09/2023
How Can Recommender Systems Benefit from Large Language Models: A Survey
Recommender systems (RS) play important roles to match users' informatio...
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06/05/2023
CTRL: Connect Tabular and Language Model for CTR Prediction
Traditional click-through rate (CTR) prediction models convert the tabul...
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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 ...
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03/21/2023
Dynamically Expandable Graph Convolution for Streaming Recommendation
Personalized recommender systems have been widely studied and deployed t...
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02/22/2023
A Survey on User Behavior Modeling in Recommender Systems
User Behavior Modeling (UBM) plays a critical role in user interest lear...
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02/07/2023
Multi-Task Deep Recommender Systems: A Survey
Multi-task learning (MTL) aims at learning related tasks in a unified mo...
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11/11/2022
Intent-aware Multi-source Contrastive Alignment for Tag-enhanced Recommendation
To offer accurate and diverse recommendation services, recent methods us...
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10/26/2022
Disentangling Past-Future Modeling in Sequential Recommendation via Dual Networks
Sequential recommendation (SR) plays an important role in personalized r...
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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...
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09/05/2022
A Brief History of Recommender Systems
Soon after the invention of the Internet, the recommender system emerged...
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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...
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08/09/2022
OptEmbed: Learning Optimal Embedding Table for Click-through Rate Prediction
Learning embedding table plays a fundamental role in Click-through rate(...
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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...
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08/02/2022
Adapting Triplet Importance of Implicit Feedback for Personalized Recommendation
Implicit feedback is frequently used for developing personalized recomme...
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06/17/2022
A Graph-Enhanced Click Model for Web Search
To better exploit search logs and model users' behavior patterns, numero...
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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...
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04/26/2022
Cross Pairwise Ranking for Unbiased Item Recommendation
Most recommender systems optimize the model on observed interaction data...
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04/04/2022
Automated Machine Learning for Deep Recommender Systems: A Survey
Deep recommender systems (DRS) are critical for current commercial onlin...
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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...
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01/27/2022
DropNAS: Grouped Operation Dropout for Differentiable Architecture Search
Neural architecture search (NAS) has shown encouraging results in automa...
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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...
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11/16/2021
On Effective Scheduling of Model-based Reinforcement Learning
Model-based reinforcement learning has attracted wide attention due to i...
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11/16/2021
QA4PRF: A Question Answering based Framework for Pseudo Relevance Feedback
Pseudo relevance feedback (PRF) automatically performs query expansion b...
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11/05/2021
AIM: Automatic Interaction Machine for Click-Through Rate Prediction
Feature embedding learning and feature interaction modeling are two cruc...
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10/25/2021
Content Filtering Enriched GNN Framework for News Recommendation
Learning accurate users and news representations is critical for news re...
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10/18/2021
Context-aware Reranking with Utility Maximization for Recommendation
As a critical task for large-scale commercial recommender systems, reran...
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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...
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08/11/2021
Retrieval Interaction Machine for Tabular Data Prediction
Prediction over tabular data is an essential task in many data science a...
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06/25/2021
Balancing Accuracy and Fairness for Interactive Recommendation with Reinforcement Learning
Fairness in recommendation has attracted increasing attention due to bia...
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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...
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06/01/2021
Dual Graph enhanced Embedding Neural Network for CTR Prediction
CTR prediction, which aims to estimate the probability that a user will ...
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04/21/2021
Deep Learning for Click-Through Rate Estimation
Click-through rate (CTR) estimation plays as a core function module in v...
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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,...
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04/13/2021
An Adversarial Imitation Click Model for Information Retrieval
Modern information retrieval systems, including web search, ads placemen...
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01/13/2021
Probabilistic Metric Learning with Adaptive Margin for Top-K Recommendation
Personalized recommender systems are playing an increasingly important r...
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12/16/2020
AutoDis: Automatic Discretization for Embedding Numerical Features in CTR Prediction
Learning sophisticated feature interactions is crucial for Click-Through...
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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...
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09/04/2020
A Practical Incremental Method to Train Deep CTR Models
Deep learning models in recommender systems are usually trained in the b...
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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 ...
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08/25/2020
GraphSAIL: Graph Structure Aware Incremental Learning for Recommender Systems
Given the convenience of collecting information through online services,...
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06/18/2020
