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of 8
pro vyhledávání: '"Pancha, Nikil"'
Autor:
Agarwal, Prabhat, Sk, Minhazul Islam, Pancha, Nikil, Hazra, Kurchi Subhra, Xu, Jiajing, Rosenberg, Chuck
In this paper, we present OmniSearchSage, a versatile and scalable system for understanding search queries, pins, and products for Pinterest search. We jointly learn a unified query embedding coupled with pin and product embeddings, leading to an imp
Externí odkaz:
http://arxiv.org/abs/2404.16260
Autor:
Xia, Xue, Eksombatchai, Pong, Pancha, Nikil, Badani, Dhruvil Deven, Wang, Po-Wei, Gu, Neng, Joshi, Saurabh Vishwas, Farahpour, Nazanin, Zhang, Zhiyuan, Zhai, Andrew
Sequential models that encode user activity for next action prediction have become a popular design choice for building web-scale personalized recommendation systems. Traditional methods of sequential recommendation either utilize end-to-end learning
Externí odkaz:
http://arxiv.org/abs/2306.00248
Publikováno v:
KDD 2022
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 ItemSage to provide relevant recommendations in all shopping use cases i
Externí odkaz:
http://arxiv.org/abs/2205.11728
Autor:
Gurukar, Saket, Pancha, Nikil, Zhai, Andrew, Kim, Eric, Hu, Samson, Parthasarathy, Srinivasan, Rosenberg, Charles, Leskovec, Jure
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 tasks such as recommendation and search. At Pinterest, we have develop
Externí odkaz:
http://arxiv.org/abs/2205.10666
Sequential models have become increasingly popular in powering personalized recommendation systems over the past several years. These approaches traditionally model a user's actions on a website as a sequence to predict the user's next action. While
Externí odkaz:
http://arxiv.org/abs/2205.04507
Akademický článek
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Publikováno v:
Machine Learning; Jan2020, Vol. 109 Issue 1, p51-78, 28p
