Search Results for author:
Found 11 papers, 1 papers with code
Complete the Look: Scene-based Complementary Product Recommendation
We design an approach to extract training data for this task, and propose a novel way to learn the scene-product compatibility from fashion or interior design images.
Towards a Semantic Perceptual Image Metric
We present a full reference, perceptual image metric based on VGG-16, an artificial neural network trained on object classification.
Learning Semantic Relationships for Better Action Retrieval in Images
Human actions capture a wide variety of interactions between people and objects.
Learning a Unified Embedding for Visual Search at Pinterest
The solution we present not only allows us to train for multiple application objectives in a single deep neural network architecture, but takes advantage of correlated information in the combination of all training data from each application to generate a unified embedding that outperforms all specialized embeddings previously deployed for each product.
Shop The Look: Building a Large Scale Visual Shopping System at Pinterest
As online content becomes ever more visual, the demand for searching by visual queries grows correspondingly stronger.
PinnerSage: Multi-Modal User Embedding Framework for Recommendations at Pinterest
Latent user representations are widely adopted in the tech industry for powering personalized recommender systems.
PinnerFormer: Sequence Modeling for User Representation at Pinterest
Sequential models have become increasingly popular in powering personalized recommendation systems over the past several years.
MultiBiSage: A Web-Scale Recommendation System Using Multiple Bipartite Graphs at Pinterest
MultiBiSage can capture the graph structure of multiple bipartite graphs to learn high-quality pin embeddings.
ItemSage: Learning Product Embeddings for Shopping Recommendations at Pinterest
Learned embeddings for products are an important building block for web-scale e-commerce recommendation systems.
Modeling User Behavior With Interaction Networks for Spam Detection
Users' complex behavior can be well represented by a heterogeneous graph rich with node and edge attributes.
Rethinking Personalized Ranking at Pinterest: An End-to-End Approach
In this work, we present our journey to revolutionize the personalized recommendation engine through end-to-end learning from raw user actions.
