26 citations · 62 across the 18 of their papers we have counts for
18 papers
UniPinRec: Unifying Generative Retrieval and Ranking at Pinterest Scale
Hanyu Li, Yi-Ping Hsu, Aditya Mantha +17
Modern recommendation systems predominantly train retrieval and ranking as separate models despite both increasingly relying on large transformers encoding the same user behavior d…
LLMs Need Encoders for Semantic IDs Too
Xiangyi Chen, Zelun Wang, Xinyi Li +3
Multimodal LLMs use dedicated encoders to bridge non-language modalities (vision encoders for images, depth models for audio codec tokens) because raw token embeddings alone cannot…
A Production-Ready RL Framework for Personalized Utility Tuning with Pareto Sweeping in Pinterest Recommender Systems
Yichu Zhou, Mehdi Ben Ayed, Lin Yang +9
Large-scale recommenders encode multi-objective trade-offs by combining multiple predicted outcomes into a single utility score. Although this utility layer can be updated independ…
Deep Reinforcement Learning for Ranking Utility Tuning in the Ad Recommender System at Pinterest
Xiao Yang, Mehdi Ben Ayed, Longyu Zhao +8
The ranking utility function in an ad recommender system, which linearly combines predictions of various business goals, plays a central role in balancing values across the platfor…
Taming the One-Epoch Phenomenon in Online Recommendation System by Two-stage Contrastive ID Pre-training
Yi-Ping Hsu, Po-Wei Wang, Chantat Eksombatchai +1
ID-based embeddings are widely used in web-scale online recommendation systems. However, their susceptibility to overfitting, particularly due to the long-tail nature of data distr…
Bootstrapping Conditional Retrieval for User-to-Item Recommendations
Hongtao Lin, Haoyu Chen, Jaewon Jang +1
User-to-item retrieval has been an active research area in recommendation system, and two tower models are widely adopted due to model simplicity and serving efficiency. In this wo…
PinFM: Foundation Model for User Activity Sequences at a Billion-scale Visual Discovery Platform
Xiangyi Chen, Kousik Rajesh, Matthew Lawhon +9
User activity sequences have emerged as one of the most important signals in recommender systems. We present a foundational model, PinFM, for understanding user activity sequences…
RecoMind: A Reinforcement Learning Framework for Optimizing In-Session User Satisfaction in Recommendation Systems
Mehdi Ben Ayed, Fei Feng, Jay Adams +3
Existing web-scale recommendation systems commonly use supervised learning methods that prioritize immediate user feedback. Although reinforcement learning (RL) offers a solution t…
TransAct V2: Lifelong User Action Sequence Modeling on Pinterest Recommendation
Xue Xia, Saurabh Vishwas Joshi, Kousik Rajesh +6
Modeling user action sequences has become a popular focus in industrial recommendation system research, particularly for Click-Through Rate (CTR) prediction tasks. However, industr…
Privacy Preserving Conversion Modeling in Data Clean Room
Kungang Li, Xiangyi Chen, Ling Leng +3
In the realm of online advertising, accurately predicting the conversion rate (CVR) is crucial for enhancing advertising efficiency and user satisfaction. This paper addresses the…
OmniSage: Large Scale, Multi-Entity Heterogeneous Graph Representation Learning
Anirudhan Badrinath, Alex Yang, Kousik Rajesh +5
Representation learning, a task of learning latent vectors to represent entities, is a key task in improving search and recommender systems in web applications. Various representat…
PinRec: Unified Generative Retrieval for Pinterest Recommender Systems
Edoardo Botta, Jaewon Yang, Yi-Ping Hsu +6
Generative retrieval methods employ sequential modeling techniques, like transformers, to generate candidate items for recommender systems. These methods have demonstrated promisin…
Unified Preference Optimization: Language Model Alignment Beyond the Preference Frontier
Anirudhan Badrinath, Prabhat Agarwal, Jiajing Xu
For aligning large language models (LLMs), prior work has leveraged reinforcement learning via human feedback (RLHF) or variations of direct preference optimization (DPO). While DP…
OmniSearchSage: Multi-Task Multi-Entity Embeddings for Pinterest Search
Prabhat Agarwal, Minhazul Islam Sk, Nikil Pancha +3
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 quer…
Rethinking Personalized Ranking at Pinterest: An End-to-End Approach
Jiajing Xu, Andrew Zhai, Charles Rosenberg
In this work, we present our journey to revolutionize the personalized recommendation engine through end-to-end learning from raw user actions. We encode user's long-term interest…
Evolution of a Web-Scale Near Duplicate Image Detection System
Andrey Gusev, Jiajing Xu
Detecting near duplicate images is fundamental to the content ecosystem of photo sharing web applications. However, such a task is challenging when involving a web-scale image corp…
Training and Evaluating Multimodal Word Embeddings with Large-scale Web Annotated Images
Junhua Mao, Jiajing Xu, Yushi Jing +1
In this paper, we focus on training and evaluating effective word embeddings with both text and visual information. More specifically, we introduce a large-scale dataset with 300 m…
Visual Search at Pinterest
Yushi Jing, David Liu, Dmitry Kislyuk +4
We demonstrate that, with the availability of distributed computation platforms such as Amazon Web Services and open-source tools, it is possible for a small engineering team to bu…