3 citations · 3 across the 4 of their papers we have counts for
5 papers
PinGER: Token-Efficient Generative Retrieval with Semantic IDs
Xinyi Li, Sujan Reddy Ale, Yi-Ping Hsu +12
Generative retrieval replaces nearest-neighbor search with token-by-token decoding of an item's semantic ID (SID). But existing SID-based methods primarily use L levels of tokens s…
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…
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…
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…