arXiv AI
Entity Representation Learning Through Onsite-Offsite Graph for Pinterest Ads
Read the original on arXiv AI →The Flow has not summarised this story yet — read it at arXiv AI.
Related stories
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
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.
arXiv AI
Jun 2
On the Theoretical Limitations of Embedding-based Link Prediction
arXiv:2506. 22271v3 Announce Type: replace Abstract: Neural networks often map low-dimensional embeddings to high-dimensional output spaces.
arXiv Machine Learning
Sep 10
A Multi-Source Ensemble Approach to Candidate Generation for Alternative Vacation Rental Property Recommendations
The paper studies candidate generation for alternative vacation rental recommendations, comparing collaborative filtering, shallow embeddings, and graph neural network (GNN) methods on a platform with over 2 million active properties. A hybrid model that combines item-based collaborative filtering with GNN-based retrieval achieves a 14.8% higher Recall@300 than the best baseline, leveraging each method’s strengths: collaborative filtering for well-interacted properties and GNNs for diverse, cold-start alternatives. The authors also show that stronger candidate pools improve downstream ranking quality, though the exact impact is intertwined with ranker training.