PDF] PinnerFormer: Sequence Modeling for User Representation at Pinterest

PinnerFormer is introduced, a user representation trained to predict a users' future long-term engagement using a sequential model of a user's recent actions, and it is shown that by doing so, it significantly closes the gap between batch user embeddings that are generated once a day and realtime user embedDings generated whenever a user takes an action. 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 theoretically simplistic, these models are quite challenging to deploy in production, commonly requiring streaming infrastructure to reflect the latest user activity and potentially managing mutable data for encoding a user's hidden state. Here we introduce PinnerFormer, a user representation trained to predict a user's future long-term engagement using a sequential model of a user's recent actions. Unlike prior approaches, we adapt our modeling to a batch infrastructure via our new dense all-action loss, modeling long-term future actions instead of next action prediction. We show that by doing so, we significantly close the gap between batch user embeddings that are generated once a day and realtime user embeddings generated whenever a user takes an action. We describe our design decisions via extensive offline experimentation and ablations and validate the efficacy of our approach in A/B experiments showing substantial improvements in Pinterest's user retention and engagement when comparing PinnerFormer against our previous user representation. PinnerFormer is deployed in production as of Fall 2021.

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

How Pinterest Leverages Realtime User Actions in Recommendation to Boost Homefeed Engagement Volume, by Pinterest Engineering, Pinterest Engineering Blog

PDF] PinnerFormer: Sequence Modeling for User Representation at Pinterest

PDF) TransAct: Transformer-based Realtime User Action Model for Recommendation at Pinterest

Paper Review: ItemSage(Pinterest), by Mukul Ranjan

PDF] PinnerFormer: Sequence Modeling for User Representation at Pinterest

GitHub - pinterest/transformer_user_action: Transformer-based Realtime User Action Model for Recommendation at Pinterest

PDF] PinnerFormer: Sequence Modeling for User Representation at Pinterest

machinelearning #datascience #artificialintelligence - Damien Benveniste, PhD sa LinkedIn

TransAct: Transformer-based Realtime User Action Model for Recommendation at Pinterest

Rethinking Personalized Ranking at Pinterest: An End-to-End Approach – arXiv Vanity

Candidate Generation Using a Two Tower Approach With Expedia Group Traveler Data, by Eric Rincon, Expedia Group Technology, Dec, 2023

PDF) PinnerSage: Multi-Modal User Embedding Framework for Recommendations at Pinterest

PDF) ItemSage: Learning Product Embeddings for Shopping Recommendations at Pinterest