From the 1 of 5 linked papers with an AI index.
5 papers
MESH: Scaling Up Retrieval with Heterogeneous Content Unification
Jiaxing Qu, Yilin Chen, Junpeng Hou +4
The paper introduces MESH, a unified framework that reduces bias in heterogeneous large‑scale retrieval systems by modularizing feature spaces and applying gated bias correction, l…
PinCLIP: Large-scale Foundational Multimodal Representation at Pinterest
Josh Beal, Eric Kim, Jinfeng Rao +3
While multi-modal Visual Language Models (VLMs) have demonstrated significant success across various domains, the integration of VLMs into recommendation and retrieval systems rema…
Generative Engine Optimization: A VLM and Agent Framework for Pinterest Acquisition Growth
Faye Zhang, Qianyu Cheng, Jasmine Wan +3
Large Language Models are fundamentally reshaping content discovery through AI-native search systems such as ChatGPT, Gemini, and Claude. Unlike traditional search engines that mat…
PinLanding: Content-First Keyword Landing Page Generation via Multi-Modal AI for Web-Scale Discovery
Faye Zhang, Jasmine Wan, Qianyu Cheng +1
Online platforms like Pinterest hosting vast content collections traditionally rely on manual curation or user-generated search logs to create keyword landing pages (KLPs) -- topic…
Improving Pinterest Search Relevance Using Large Language Models
Han Wang, Mukuntha Narayanan Sundararaman, Onur Gungor +5
To improve relevance scoring on Pinterest Search, we integrate Large Language Models (LLMs) into our search relevance model, leveraging carefully designed text representations to p…