Search Results for author:
Found 8 papers, 3 papers with code
Boosting Multimedia Recommendation via Separate Generic and Unique Awareness
Furthermore, in the distancing module, we aim to distance the unique modalities from the modal-generic so that each modality retains its unique and complementary information.
Graph Bottlenecked Social Recommendation
In this paper, we focus on learning the denoised social structure to facilitate recommendation tasks from an information bottleneck perspective.
Double Correction Framework for Denoising Recommendation
To tackle the above limitations, we propose a Double Correction Framework for Denoising Recommendation (DCF), which contains two correction components from views of more precise sample dropping and avoiding more sparse data.
Improving Cognitive Diagnosis Models with Adaptive Relational Graph Neural Networks
Typically, these CD algorithms assist students by inferring their abilities (i. e., their proficiency levels on various knowledge concepts).
Generative Contrastive Graph Learning for Recommendation
Second, feature augmentation imposes the same scale noise augmentation on each node, which neglects the unique characteristics of nodes on the graph.
Joint Item Recommendation and Attribute Inference: An Adaptive Graph Convolutional Network Approach
Therefore, item recommendation and attribute inference have become two main tasks in these platforms.
Learning to Transfer Graph Embeddings for Inductive Graph based Recommendation
The transfer network is designed to approximate the learned item embeddings from graph neural networks by taking each item's visual content as input, in order to tackle the new segment problem in the test phase.
Personalized Multimedia Item and Key Frame Recommendation
We argue that the key challenge of this problem lies in discovering users' visual profiles for key frame recommendation, as most recommendation models would fail without any users' fine-grained image behavior.
