A survey of graph neural networks for recommender systems: Challenges, methods, and directions
Recommender system is one of the most important information services on today's Internet.
Recently, graph neural networks have become the new state-of-the-art approach to …
Recently, graph neural networks have become the new state-of-the-art approach to …
Graph neural networks in recommender systems: a survey
With the explosive growth of online information, recommender systems play a key role to
alleviate such information overload. Due to the important application value of recommender …
alleviate such information overload. Due to the important application value of recommender …
Towards open-world recommendation with knowledge augmentation from large language models
Recommender system plays a vital role in various online services. However, its insulated
nature of training and deploying separately within a specific closed domain limits its access …
nature of training and deploying separately within a specific closed domain limits its access …
Self-supervised graph learning for recommendation
Representation learning on user-item graph for recommendation has evolved from using
single ID or interaction history to exploiting higher-order neighbors. This leads to the …
single ID or interaction history to exploiting higher-order neighbors. This leads to the …
GNN-based long and short term preference modeling for next-location prediction
Next-location prediction is a special task of the next POIs recommendation. Different from
general recommendation tasks, next-location prediction is highly context-dependent:(1) …
general recommendation tasks, next-location prediction is highly context-dependent:(1) …
Every document owns its structure: Inductive text classification via graph neural networks
Text classification is fundamental in natural language processing (NLP), and Graph Neural
Networks (GNN) are recently applied in this task. However, the existing graph-based works …
Networks (GNN) are recently applied in this task. However, the existing graph-based works …
Gnnguard: Defending graph neural networks against adversarial attacks
Deep learning methods for graphs achieve remarkable performance on many tasks.
However, despite the proliferation of such methods and their success, recent findings …
However, despite the proliferation of such methods and their success, recent findings …
Click-through rate prediction in online advertising: A literature review
Y Yang, P Zhai - Information Processing & Management, 2022 - Elsevier
Predicting the probability that a user will click on a specific advertisement has been a
prevalent issue in online advertising, attracting much research attention in the past decades …
prevalent issue in online advertising, attracting much research attention in the past decades …
FinalMLP: an enhanced two-stream MLP model for CTR prediction
Click-through rate (CTR) prediction is one of the fundamental tasks in online advertising and
recommendation. Multi-layer perceptron (MLP) serves as a core component in many deep …
recommendation. Multi-layer perceptron (MLP) serves as a core component in many deep …
When do flat minima optimizers work?
Recently, flat-minima optimizers, which seek to find parameters in low-loss neighborhoods,
have been shown to improve a neural network's generalization performance over stochastic …
have been shown to improve a neural network's generalization performance over stochastic …