SMEF: Social-aware Multi-dimensional Edge Features-based Graph Representation Learning for Recommendation
Exploring user-item interaction cues is crucial for the performance of recommender systems.
Explicit investigation of interaction cues is made possible by using graph-based models …
Explicit investigation of interaction cues is made possible by using graph-based models …
HyperMatch: long-form text matching via hypergraph convolutional networks
Semantic text matching plays a vital role in diverse domains, such as information retrieval,
question answering, and recommendation. However, longer texts present challenges …
question answering, and recommendation. However, longer texts present challenges …
Predicting information diffusion using the inter-and intra-path of influence transitivity
Predicting information diffusion helps grasp the overall preference of user interactions,
facilitating applications such as public opinion analysis and online marketing. Existing …
facilitating applications such as public opinion analysis and online marketing. Existing …
Category-aware self-supervised graph neural network for session-based recommendation
Session-based recommendation which focuses on predicting the next behavior according to
anonymous sessions of behavior records plays an important role in real-world applications …
anonymous sessions of behavior records plays an important role in real-world applications …
Dual-Channel Latent Factor Analysis Enhanced Graph Contrastive Learning for Recommendation
J Long, H Wu - arXiv preprint arXiv:2408.04838, 2024 - arxiv.org
Graph Neural Networks (GNNs) are powerful learning methods for recommender systems
owing to their robustness in handling complicated user-item interactions. Recently, the …
owing to their robustness in handling complicated user-item interactions. Recently, the …
Towards Bridging the Cross-modal Semantic Gap for Multi-modal Recommendation
Multi-modal recommendation greatly enhances the performance of recommender systems
by modeling the auxiliary information from multi-modality contents. Most existing multi-modal …
by modeling the auxiliary information from multi-modality contents. Most existing multi-modal …
Periodicity aware spatial-temporal adaptive hypergraph neural network for traffic forecasting
Traffic forecasting is the foundation and core task of Intelligent Transportation Systems (ITS).
Due to the powerful ability of Graph Neural Network (GNN) to capture topological features …
Due to the powerful ability of Graph Neural Network (GNN) to capture topological features …
A Representation Learning Link Prediction Approach Using Line Graph Neural Networks
Link prediction problem aims to infer the potential future links between two nodes in the
network. Most of the existing methods exhibit limited universality and are only effective in …
network. Most of the existing methods exhibit limited universality and are only effective in …
How to Bridge Spatial and Temporal Heterogeneity in Link Prediction? A Contrastive Method
Temporal Heterogeneous Networks play a crucial role in capturing the dynamics and
heterogeneity inherent in various real-world complex systems, rendering them a noteworthy …
heterogeneity inherent in various real-world complex systems, rendering them a noteworthy …
Topic-Aware Masked Attentive Network for Information Cascade Prediction
Predicting information cascades holds significant practical implications, including
applications in public opinion analysis, rumor control, and product recommendation. Existing …
applications in public opinion analysis, rumor control, and product recommendation. Existing …