141 citations · 279 across the 13 of their papers we have counts for
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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…