arxiv preprint – Recommender Systems with Generative Retrieval

In this episode we discuss Recommender Systems with Generative Retrieval
by Shashank Rajput, Nikhil Mehta, Anima Singh, Raghunandan H. Keshavan, Trung Vu, Lukasz Heldt, Lichan Hong, Yi Tay, Vinh Q. Tran, Jonah Samost, Maciej Kula, Ed H. Chi, Maheswaran Sathiamoorthy. The paper presents a novel generative approach for large-scale retrieval in recommender systems, where a model autoregressively decodes the identifiers (Semantic IDs) of target items. It introduces Semantic IDs, composed of semantically meaningful tuples, to represent items, and uses a Transformer-based sequence-to-sequence model to predict the next item a user will interact with based on their session history. The approach outperforms current state-of-the-art models on multiple datasets and demonstrates improved generalization, effectively retrieving items without prior interactions.