Search Results for author:
Found 13 papers, 3 papers with code
TransAct: Transformer-based Realtime User Action Model for Recommendation at Pinterest
This paper (1) presents Pinterest's ranking architecture for Homefeed, our personalized recommendation product and the largest engagement surface; (2) proposes TransAct, a sequential model that extracts users' short-term preferences from their realtime activities; (3) describes our hybrid approach to ranking, which combines end-to-end sequential modeling via TransAct with batch-generated user embeddings.
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.
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.
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.
PinnerFormer: Sequence Modeling for User Representation at Pinterest
Sequential models have become increasingly popular in powering personalized recommendation systems over the past several years.
Billion-Scale Pretraining with Vision Transformers for Multi-Task Visual Representations
Large-scale pretraining of visual representations has led to state-of-the-art performance on a range of benchmark computer vision tasks, yet the benefits of these techniques at extreme scale in complex production systems has been relatively unexplored.
Ranked #26 on
Image Classification
on ObjectNet
(using extra training data)
Toward Transformer-Based Object Detection
The Vision Transformer was the first major attempt to apply a pure transformer model directly to images as input, demonstrating that as compared to convolutional networks, transformer-based architectures can achieve competitive results on benchmark classification tasks.
Bootstrapping Complete The Look at Pinterest
In this paper, we will describe how we bootstrapped the Complete The Look (CTL) system at Pinterest.
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.
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.
Classification is a Strong Baseline for Deep Metric Learning
Deep metric learning aims to learn a function mapping image pixels to embedding feature vectors that model the similarity between images.
Ranked #3 on
Image Retrieval
on CARS196
Visual Discovery at Pinterest
Over the past three years Pinterest has experimented with several visual search and recommendation services, including Related Pins (2014), Similar Looks (2015), Flashlight (2016) and Lens (2017).
Visual Search at Pinterest
We demonstrate that, with the availability of distributed computation platforms such as Amazon Web Services and open-source tools, it is possible for a small engineering team to build, launch and maintain a cost-effective, large-scale visual search system with widely available tools.
