Generative Recommendation with Semantic IDs: A Practitioner's Handbook
Clark Mingxuan Ju, Liam Collins, Leonardo Neves, Bhuvesh Kumar, Louis Yufeng Wang, Tong Zhao, Neil Shah

TL;DR
This paper introduces GRID, an open-source framework for generative recommendation models using semantic IDs, enabling systematic benchmarking, analysis of components, and accelerating research in the field.
Contribution
The paper presents a modular, open-source framework for generative recommendation with semantic IDs, facilitating standardized evaluation and faster development.
Findings
Architectural components significantly affect model performance.
Systematic ablation reveals key factors influencing effectiveness.
Open-source platform accelerates research and benchmarking.
Abstract
Generative recommendation (GR) has gained increasing attention for its promising performance compared to traditional models. A key factor contributing to the success of GR is the semantic ID (SID), which converts continuous semantic representations (e.g., from large language models) into discrete ID sequences. This enables GR models with SIDs to both incorporate semantic information and learn collaborative filtering signals, while retaining the benefits of discrete decoding. However, varied modeling techniques, hyper-parameters, and experimental setups in existing literature make direct comparisons between GR proposals challenging. Furthermore, the absence of an open-source, unified framework hinders systematic benchmarking and extension, slowing model iteration. To address this challenge, our work introduces and open-sources a framework for Generative Recommendation with semantic ID,…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
