Search Results for author: Charles Rosenberg

Found 11 papers, 1 papers with code

Rethinking Personalized Ranking at Pinterest: An End-to-End Approach

no code implementations18 Sep 2022 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.

Modeling User Behavior With Interaction Networks for Spam Detection

no code implementations21 Jul 2022 Prabhat Agarwal, Manisha Srivastava, Vishwakarma Singh, Charles Rosenberg

Users' complex behavior can be well represented by a heterogeneous graph rich with node and edge attributes.

Spam detection

PinnerFormer: Sequence Modeling for User Representation at Pinterest

no code implementations9 May 2022 Nikil Pancha, Andrew Zhai, Jure Leskovec, Charles Rosenberg

Sequential models have become increasingly popular in powering personalized recommendation systems over the past several years.

Recommendation Systems

Learning a Unified Embedding for Visual Search at Pinterest

no code implementations5 Aug 2019 Andrew Zhai, Hao-Yu Wu, Eric Tzeng, Dong Huk Park, Charles Rosenberg

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.

Metric Learning Navigate +2

Complete the Look: Scene-based Complementary Product Recommendation

1 code implementation CVPR 2019 Wang-Cheng Kang, Eric Kim, Jure Leskovec, Charles Rosenberg, Julian McAuley

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.

Product Recommendation

Towards a Semantic Perceptual Image Metric

no code implementations1 Aug 2018 Troy Chinen, Johannes Ballé, Chunhui Gu, Sung Jin Hwang, Sergey Ioffe, Nick Johnston, Thomas Leung, David Minnen, Sean O'Malley, Charles Rosenberg, George Toderici

We present a full reference, perceptual image metric based on VGG-16, an artificial neural network trained on object classification.

Image Quality Assessment

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