141 citations · 279 across the 13 of their papers we have counts for
13 papers
TransAct: Transformer-based Realtime User Action Model for Recommendation at Pinterest
Xue Xia, Pong Eksombatchai, Nikil Pancha +7
Sequential models that encode user activity for next action prediction have become a popular design choice for building web-scale personalized recommendation systems. Traditional m…
Rethinking Personalized Ranking at Pinterest: An End-to-End Approach
Jiajing Xu, Andrew Zhai, Charles Rosenberg
In this work, we present our journey to revolutionize the personalized recommendation engine through end-to-end learning from raw user actions. We encode user's long-term interest…
ItemSage: Learning Product Embeddings for Shopping Recommendations at Pinterest
Paul Baltescu, Haoyu Chen, Nikil Pancha +3
Learned embeddings for products are an important building block for web-scale e-commerce recommendation systems. At Pinterest, we build a single set of product embeddings called It…
MultiBiSage: A Web-Scale Recommendation System Using Multiple Bipartite Graphs at Pinterest
Saket Gurukar, Nikil Pancha, Andrew Zhai +5
Graph Convolutional Networks (GCN) can efficiently integrate graph structure and node features to learn high-quality node embeddings. These embeddings can then be used for several…
PinnerFormer: Sequence Modeling for User Representation at Pinterest
Nikil Pancha, Andrew Zhai, Jure Leskovec +1
Sequential models have become increasingly popular in powering personalized recommendation systems over the past several years. These approaches traditionally model a user's action…
Billion-Scale Pretraining with Vision Transformers for Multi-Task Visual Representations
Josh Beal, Hao-Yu Wu, Dong Huk Park +2
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 ext…
Toward Transformer-Based Object Detection
Josh Beal, Eric Kim, Eric Tzeng +3
Transformers have become the dominant model in natural language processing, owing to their ability to pretrain on massive amounts of data, then transfer to smaller, more specific t…
Bootstrapping Complete The Look at Pinterest
Eileen Li, Eric Kim, Andrew Zhai +2
Putting together an ideal outfit is a process that involves creativity and style intuition. This makes it a particularly difficult task to automate. Existing styling products gener…
Shop The Look: Building a Large Scale Visual Shopping System at Pinterest
Raymond Shiau, Hao-Yu Wu, Eric Kim +7
As online content becomes ever more visual, the demand for searching by visual queries grows correspondingly stronger. Shop The Look is an online shopping discovery service at Pint…
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…
Classification is a Strong Baseline for Deep Metric Learning
Andrew Zhai, Hao-Yu Wu
Deep metric learning aims to learn a function mapping image pixels to embedding feature vectors that model the similarity between images. Two major applications of metric learning…
Visual Discovery at Pinterest
Andrew Zhai, Dmitry Kislyuk, Yushi Jing +5
Over the past three years Pinterest has experimented with several visual search and recommendation services, including Related Pins (2014), Similar Looks (2015), Flashlight (2016)…
Visual Search at Pinterest
Yushi Jing, David Liu, Dmitry Kislyuk +4
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 bu…