arXiv AI
Entity Representation Learning Through Onsite-Offsite Graph for Pinterest Ads
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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
Jul 29
TRWH: A Text-Driven Random Walk Heterogeneous GNN for Semantic-Aware Sparse Recommendation
arXiv:2607. 25471v1 Announce Type: new Abstract: Graph Neural Networks (GNNs) and Large Language Models (LLMs) have each advanced recommendation systems by modeling structural and semantic signals, respectively.
arXiv AI
Sep 30
ImbalancE: Inference-Time Latent Search Against Degree Imbalance in Link Prediction
The paper introduces ImbalancE, an inference‑time latent search method that mitigates degree imbalance bias in Knowledge Graph Embedding models. It targets the problematic prediction of target entities with much lower degrees than anchor entities, a common issue in recommender systems and other applications. Experiments on benchmark datasets show that ImbalancE improves predictions on the most imbalanced triples compared to conventional methods.
arXiv Machine Learning
Oct 21
Rethinking Generalization in Knowledge Graph Embedding: An Empirical Study and Sparse Peak Regularization
arXiv:2505.23442v2 Announce Type: replace Abstract: Knowledge graphs (KGs) provide structured representations of entities and relations, and underpin data management and Web applications such as sear...
arXiv AI
Jul 28
TextBridgeGNN: Pre-training Graph Neural Network for Cross-Domain Recommendation via Text-Guided Transfer
arXiv:2601. 02366v3 Announce Type: replace-cross Abstract: Graph-based recommendation has achieved great success in recent years.
arXiv Machine Learning
Aug 28
Scaling Graph Neural Networks for Friend Recommendation: Multi-Hash User Embeddings and Temporal Neighbor Sampling
The paper presents a scalable graph neural network (GNN) system for friend recommendation on a massive social graph. It introduces two key design choices: multi-hash ID embeddings that shrink the embedding table by over 98% without hurting ranking quality, and a timestamp-sorted compressed sparse row (CSR) storage with binary search that reduces temporal neighbor sampling from linear to logarithmic time. Experiments on a 194‑million‑user, 28‑billion‑edge graph show that these techniques enable production‑grade performance, boosting friend additions by 16% and unique friend adders by 11.5% in an online A/B test.