Add to ORKGIn recommender systems, leveraging Graph Neural Networks (GNNs) to formulate the bipartite relation between users and items is a promising way. However, powerful negative sampling methods that is adapted to GNN-based recommenders still requires a lot of efforts. One critical gap is that it is rather tough to distinguish real negatives from massive unobserved items during hard negative sampling. Towards this problem, this paper develops a novel hard negative sampling method for GNN-based recommendation systems by simply reformulating the loss function. We conduct various experiments on three datasets, demonstrating that the method proposed outperforms a set of state-of-the-art benchmarks.Comment: 9 pages, 16 figure
Graph contrastive learning has emerged as a powerful tool for unsupervised graph representation lear...
In recommender systems, users rate items, and are subsequently served other product recommendations ...
Recommender systems learn from historical user-item interactions to identify preferred items for tar...
Collaborative filtering (CF), as a standard method for recommendation with implicit feedback, tackle...
How to sample high quality negative instances from unlabeled data, i.e., negative sampling, is impor...
Collaborative filtering techniques rely on aggregated user preference data to make personalized pred...
Recent studies on recommendation have largely focused on exploring state-of-the-art neural networks ...
Recommender Systems (RSs) are used to provide users with personalized item recommendations and help ...
We study the problem of designing hard negative sampling distributions for unsupervised contrastive ...
University of Technology Sydney. Faculty of Engineering and Information Technology.Convolutional neu...
Contrastive deep graph clustering, which aims to divide nodes into disjoint groups via contrastive m...
Negative sampling (NS) loss plays an important role in learning knowledge graph embedding (KGE) to h...
A large catalogue size is one of the central challenges in training recommendation models: a large n...
Although the word-popularity based negative sampler has shown superb performance in the skip-gram mo...
The Recommender system is a vital information service on today's Internet. Recently, graph neural ne...
Graph contrastive learning has emerged as a powerful tool for unsupervised graph representation lear...
In recommender systems, users rate items, and are subsequently served other product recommendations ...
Recommender systems learn from historical user-item interactions to identify preferred items for tar...
Collaborative filtering (CF), as a standard method for recommendation with implicit feedback, tackle...
How to sample high quality negative instances from unlabeled data, i.e., negative sampling, is impor...
Collaborative filtering techniques rely on aggregated user preference data to make personalized pred...
Recent studies on recommendation have largely focused on exploring state-of-the-art neural networks ...
Recommender Systems (RSs) are used to provide users with personalized item recommendations and help ...
We study the problem of designing hard negative sampling distributions for unsupervised contrastive ...
University of Technology Sydney. Faculty of Engineering and Information Technology.Convolutional neu...
Contrastive deep graph clustering, which aims to divide nodes into disjoint groups via contrastive m...
Negative sampling (NS) loss plays an important role in learning knowledge graph embedding (KGE) to h...
A large catalogue size is one of the central challenges in training recommendation models: a large n...
Although the word-popularity based negative sampler has shown superb performance in the skip-gram mo...
The Recommender system is a vital information service on today's Internet. Recently, graph neural ne...
Graph contrastive learning has emerged as a powerful tool for unsupervised graph representation lear...
In recommender systems, users rate items, and are subsequently served other product recommendations ...
Recommender systems learn from historical user-item interactions to identify preferred items for tar...