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Found 5 papers, 1 papers with code
OmniSearchSage: Multi-Task Multi-Entity Embeddings for Pinterest Search
In this paper, we present OmniSearchSage, a versatile and scalable system for understanding search queries, pins, and products for Pinterest search.
TransAct: Transformer-based Realtime User Action Model for Recommendation at Pinterest
This paper (1) presents Pinterest's ranking architecture for Homefeed, our personalized recommendation product and the largest engagement surface; (2) proposes TransAct, a sequential model that extracts users' short-term preferences from their realtime activities; (3) describes our hybrid approach to ranking, which combines end-to-end sequential modeling via TransAct with batch-generated user embeddings.
ItemSage: Learning Product Embeddings for Shopping Recommendations at Pinterest
Learned embeddings for products are an important building block for web-scale e-commerce recommendation systems.
MultiBiSage: A Web-Scale Recommendation System Using Multiple Bipartite Graphs at Pinterest
MultiBiSage can capture the graph structure of multiple bipartite graphs to learn high-quality pin embeddings.
PinnerFormer: Sequence Modeling for User Representation at Pinterest
Sequential models have become increasingly popular in powering personalized recommendation systems over the past several years.
