5 papers
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…
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…
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…
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…
InteractRank: Personalized Web-Scale Search Pre-Ranking with Cross Interaction Features
Sujay Khandagale, Bhawna Juneja, Prabhat Agarwal +3
Modern search systems use a multi-stage architecture to deliver personalized results efficiently. Key stages include retrieval, pre-ranking, full ranking, and blending, which refin…