no code implementations • 18 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.
no code implementations • 21 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.
no code implementations • 24 May 2022 • Paul Baltescu, Haoyu Chen, Nikil Pancha, Andrew Zhai, Jure Leskovec, Charles Rosenberg
Learned embeddings for products are an important building block for web-scale e-commerce recommendation systems.
no code implementations • 21 May 2022 • Saket Gurukar, Nikil Pancha, Andrew Zhai, Eric Kim, Samson Hu, Srinivasan Parthasarathy, Charles Rosenberg, Jure Leskovec
MultiBiSage can capture the graph structure of multiple bipartite graphs to learn high-quality pin embeddings.
no code implementations • 9 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.
no code implementations • 7 Jul 2020 • Aditya Pal, Chantat Eksombatchai, Yitong Zhou, Bo Zhao, Charles Rosenberg, Jure Leskovec
Latent user representations are widely adopted in the tech industry for powering personalized recommender systems.
no code implementations • 18 Jun 2020 • Raymond Shiau, Hao-Yu Wu, Eric Kim, Yue Li Du, Anqi Guo, Zhiyuan Zhang, Eileen Li, Kunlong Gu, Charles Rosenberg, Andrew Zhai
As online content becomes ever more visual, the demand for searching by visual queries grows correspondingly stronger.
no code implementations • 5 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.
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.
no code implementations • 1 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.
no code implementations • CVPR 2015 • Vignesh Ramanathan, Cong-Cong Li, Jia Deng, Wei Han, Zhen Li, Kunlong Gu, Yang song, Samy Bengio, Charles Rosenberg, Li Fei-Fei
Human actions capture a wide variety of interactions between people and objects.