Generative-contrastive graph learning for recommendation
By treating users' interactions as a user-item graph, graph learning models have been
widely deployed in Collaborative Filtering~(CF) based recommendation. Recently …
widely deployed in Collaborative Filtering~(CF) based recommendation. Recently …
Exploring the individuality and collectivity of intents behind interactions for graph collaborative filtering
Intent modeling has attracted widespread attention in recommender systems. As the core
motivation behind user selection of items, intent is crucial for elucidating recommendation …
motivation behind user selection of items, intent is crucial for elucidating recommendation …
Denoising heterogeneous graph pre-training framework for recommendation
Heterogeneous graph neural networks (HGNN) have exhibited significant performance
gains by modeling the information propagation process in graph-structured data for …
gains by modeling the information propagation process in graph-structured data for …
Analyzing Public Sentiment on the Amazon Website: A GSK-based Double Path Transformer Network Approach for Sentiment Analysis
Sentiment Analysis (SA) holds considerable significance in comprehending public
perspectives and conducting precise opinion-based evaluations, making it a prominent …
perspectives and conducting precise opinion-based evaluations, making it a prominent …
Intent-guided Heterogeneous Graph Contrastive Learning for Recommendation
Contrastive Learning (CL)-based recommender systems have gained prominence in the
context of Heterogeneous Graph (HG) due to their capacity to enhance the consistency of …
context of Heterogeneous Graph (HG) due to their capacity to enhance the consistency of …
Gorec: a generative cold-start recommendation framework
Multimedia-based recommendation models learn user and item preference representation
by fusing both the user-item collaborative signals and the multimedia content signals. In real …
by fusing both the user-item collaborative signals and the multimedia content signals. In real …
NIE-GCN: Neighbor Item Embedding-Aware Graph Convolutional Network for Recommendation
Graph convolutional networks (GCNs) have been widely used to learn high-quality
representations (aka embeddings) from multiorder neighbors in recommendation tasks …
representations (aka embeddings) from multiorder neighbors in recommendation tasks …
Simplify to the Limit! Embedding-less Graph Collaborative Filtering for Recommender Systems
The tremendous positive driving effect of Graph Convolutional Network (GCN) and Graph
Contrastive Learning (GCL) for recommender systems has become a consensus. GCN …
Contrastive Learning (GCL) for recommender systems has become a consensus. GCN …
A federated deep learning framework for privacy-preserving consumer electronics recommendations
Recommender systems (RSs) have proven to be highly effective in guiding consumers
towards well-informed purchase decisions for electronics. These systems can provide …
towards well-informed purchase decisions for electronics. These systems can provide …
AdaGIN: Adaptive Graph Interaction Network for Click-Through Rate Prediction
The goal of click-through rate (CTR) prediction in recommender systems is to effectively
work with input features. However, existing CTR prediction models face three main issues …
work with input features. However, existing CTR prediction models face three main issues …