PinnerSage: Multi-Modal User Embedding Framework for Recommendations at Pinterest | by Pinterest Engineering | Pinterest Engineering Blog | Medium

Aditya Pal | Applied Science, Chantat Eksombatchai | Applied Science, Yitong Zhou | User Understanding, Bo Zhao | User Understanding, Charles Rosenberg | Applied Science, Jure Leskovec | Applied Science As we build a visual discovery engine that powers 2B+ Pins, it’s crucial to understand user interests and preferences in order to serve relevant content. One standard approach to encode user preferences is via an embedding-based representation in a high dimensional space. Most prior methods tried at Pinterest infer a single high-dimensional embedding for each user in compatibility with the content embedding. This is a good starting point but falls short in delivering a full understanding of the user. In this work, we postulate that a single embedding is not sufficient for encoding multiple facets of a user’s interests that might have no obvious linkage between them. They can evolve, with some interests persisting long term while others span a short time period. Recommended items are als

Aditya Pal | Applied Science, Chantat Eksombatchai | Applied Science, Yitong Zhou | User Understanding, Bo Zhao | User Understanding, Charles Rosenberg | Applied Science, Jure Leskovec | Applied Science As we build a visual discovery engine that powers 2B+ Pins, it’s crucial to understand user interests and preferences in order to serve relevant content. One standard approach to encode user preferences is via an embedding-based representation in a high dimensional space. Most prior methods tried at Pinterest infer a single high-dimensional embedding for each user in compatibility with the cont