2.9k citations
- Stanford UniversityUS2 papers
- Beihang UniversityCN1 paper
- Inception Institute of Artificial IntelligenceAE1 paper
- Meta (United States)US1 paper
- Monash UniversityAU1 paper
- Peking UniversityCN1 paper
- Peng Cheng LaboratoryCN1 paper
- Rensselaer Polytechnic InstituteUS1 paper
- University of California, BerkeleyUS1 paper
- University of California, IrvineUS1 paper
- University of OttawaCA1 paper
- Xiamen UniversityCN1 paper
7 papers
PinnerSage: Multi-Modal User Embedding Framework for Recommendations at Pinterest
Aditya Pal, Chantat Eksombatchai, Yitong Zhou +3
Latent user representations are widely adopted in the tech industry for powering personalized recommender systems. Most prior work infers a single high dimensional embedding to rep…
Toward Subgraph-Guided Knowledge Graph Question Generation with Graph Neural Networks
Yu Chen, Lingfei Wu, Mohammed J. Zaki
Knowledge graph (KG) question generation (QG) aims to generate natural language questions from KGs and target answers. Previous works mostly focus on a simple setting which is to g…
HRank: Filter Pruning using High-Rank Feature Map
Mingbao Lin, Rongrong Ji, Yan Wang +4
Neural network pruning offers a promising prospect to facilitate deploying deep neural networks on resource-limited devices. However, existing methods are still challenged by the t…
Learning a Unified Embedding for Visual Search at Pinterest
Andrew Zhai, Hao-Yu Wu, Eric Tzeng +2
At Pinterest, we utilize image embeddings throughout our search and recommendation systems to help our users navigate through visual content by powering experiences like browsing o…
Use of OWL and Semantic Web Technologies at Pinterest
Rafael S. Gonçalves, Matthew Horridge, Rui Li +6
Pinterest is a popular Web application that has over 250 million active users. It is a visual discovery engine for finding ideas for recipes, fashion, weddings, home decoration, an…
Graph Convolutional Neural Networks for Web-Scale Recommender Systems
Rex Ying, Ruining He, Kaifeng Chen +3
Recent advancements in deep neural networks for graph-structured data have led to state-of-the-art performance on recommender system benchmarks. However, making these methods pract…