arXiv AI
Hypergraph-Enhanced Dual Convolutional Network for Bundle Recommendation
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arXiv AI
Jul 17
DualHNIE: Dual-Channel Hypergraph Learning for Node Importance Estimation in Heterogeneous Knowledge Graphs
arXiv:2512. 12477v3 Announce Type: replace Abstract: Estimating node importance in heterogeneous knowledge graphs is a fundamental problem underlying recommendation, search, and knowledge decision systems.
arXiv Machine Learning
Sep 23
TailSpec-EASE: Knowledge-Graph-Regularized Linear Recommendation for Web Long-Tail Discovery
TailSpec-EASE is a lightweight linear recommender that incorporates a relation‑aware spectral knowledge‑graph prior into a local closed‑form reconstruction objective. By adapting the prior strength to item popularity, it provides stronger semantic guidance for long‑tail items. Across four public benchmarks, it achieves a favorable balance of overall accuracy, long‑tail performance, and training cost, improving NDCG@20 by up to 24% over a no‑KG baseline and training in just 37 seconds on CPU compared to thousands of seconds for GPU‑based KGAT and CPU LightGCN.
arXiv AI
Jul 10
MetaHGNIE: Meta-Path Induced Hypergraph Contrastive Learning in Heterogeneous Knowledge Graphs
arXiv:2512. 12477v2 Announce Type: replace Abstract: Estimating node importance in heterogeneous knowledge graphs is a fundamental problem underlying recommendation, search, and knowledge decision systems.
arXiv Machine Learning
Jul 24
Cardinality-Decomposed Loss: Matching Training Objectives to Relation Structure in Heterogeneous Recommendation Graphs
arXiv:2607. 20737v1 Announce Type: new Abstract: Graph Neural Networks trained on heterogenous bipartite graphs form a common basis in recommendation systems.
arXiv AI
Jun 18
RankGraph-2: Lifecycle Co-Design for Billion-Node Graph Learning in Recommendation
arXiv:2606. 18379v1 Announce Type: cross Abstract: Graph-based retrieval at billion-node scale requires jointly solving three tightly coupled problems -- graph construction, representation learning, and real-time serving -- yet existing work addresses each in isolation.
arXiv AI
Jul 22
Mitigating Matthew Effect: Multi-Hypergraph Boosted Multi-Interest Self-Supervised Learning for Conversational Recommendation
arXiv:2607. 18609v1 Announce Type: cross Abstract: The Matthew effect is a big challenge in Recommender Systems (RSs), where popular items tend to receive increasing attention, while less popular ones are often overlooked, perpetuating existing disparities.