[HTML][HTML] Higher-order knowledge-enhanced recommendation with heterogeneous hypergraph multi-attention
Recent advancements in recommender systems have focused on integrating knowledge
graphs (KGs) to leverage their auxiliary information. The core idea of KG-enhanced …
graphs (KGs) to leverage their auxiliary information. The core idea of KG-enhanced …
Instruction-based hypergraph pretraining
Pretraining has been widely explored to augment the adaptability of graph learning models
to transfer knowledge from large datasets to a downstream task, such as link prediction or …
to transfer knowledge from large datasets to a downstream task, such as link prediction or …
Knowledge Graph Context-Enhanced Diversified Recommendation
The field of Recommender Systems (RecSys) has been extensively studied to enhance
accuracy by leveraging users' historical interactions. Nonetheless, this persistent pursuit of …
accuracy by leveraging users' historical interactions. Nonetheless, this persistent pursuit of …
Collaborative Alignment for Recommendation
Traditional recommender systems have primarily relied on identity representations (IDs) to
model users and items. Recently, the integration of pre-trained language models (PLMs) has …
model users and items. Recently, the integration of pre-trained language models (PLMs) has …
Higher-order link prediction via light hypergraph neural network and hybrid aggregator
Link prediction, which aims to predict missing links or possible future links between two
nodes, is one of the most important research in social network analysis. Higher-order link …
nodes, is one of the most important research in social network analysis. Higher-order link …
Self-supervised progressive graph neural network for enhanced multi-behavior recommendation
T Liu, H Zhou, C Li, Z Zhao - … Journal of Machine Learning and Cybernetics, 2024 - Springer
Multi-behavior recommendation (MBR) aims to enhance the accuracy of predicting target
behavior by considering multiple behaviors simultaneously. Recent researches have …
behavior by considering multiple behaviors simultaneously. Recent researches have …
[HTML][HTML] Improving healthy food recommender systems through heterogeneous hypergraph learning
Recommender systems in health-conscious recipe suggestions have evolved rapidly,
particularly with the integration of both homogeneous and heterogeneous graphs. However …
particularly with the integration of both homogeneous and heterogeneous graphs. However …
Heterogeneous Hypergraph Embedding for Recommendation Systems
Recent advancements in recommender systems have focused on integrating knowledge
graphs (KGs) to leverage their auxiliary information. The core idea of KG-enhanced …
graphs (KGs) to leverage their auxiliary information. The core idea of KG-enhanced …
Physics-guided Hypergraph Contrastive Learning for Dynamic Hyperedge Prediction
Z Wang, J Chen, M Gong, F Hao - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
With the increasing magnitude and complexity of data, the importance of higher-order
networks is increasingly prominent. Dynamic hyperedge prediction reveals potential higher …
networks is increasingly prominent. Dynamic hyperedge prediction reveals potential higher …
High-Order Recommendation with Heterophilic Hypergraph Diffusion
Collaborative Filtering (CF) is a crucial task in recommendation systems, aimed at predicting
user preferences based on the behaviors and preferences of similar users. While traditional …
user preferences based on the behaviors and preferences of similar users. While traditional …