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OneRec: Unifying Retrieve and Rank with Generative Recommender and Iterative Preference Alignment by Deng, Jiaxin; Wang, Shiyao; Cai, Kuo; Ren, Lejian; Hu, Qigen; Ding, Weifeng; Luo, Qiang; Zhou, Guorui is a scholarly article available to read on EtoBox.

What is OneRec: Unifying Retrieve and Rank with Generative Recommender and Iterative Preference Alignment about?

Recently, generative retrieval-based recommendation systems have emerged as a promising paradigm. However, most modern recommender systems adopt a retrieve-and-rank strategy, where the generative model functions only as a selector during the retrieval stage. In this paper, we propose OneRec, which replaces the cascaded learning framework with a unified generative model. To the best of our knowledge, this is the first end-to-end generative model that significantly surpasses current complex and well-designed recommender systems in real-world scenarios. Specifically, OneRec includes: 1) an encoder-decoder structure, which encodes the user's historical behavior sequences and gradually decodes the videos that the user may be interested in. We adopt sparse Mixture-of-Experts (MoE) to scale model capacity without proportionally increasing computational FLOPs. 2) a session-wise generation approach. In contrast to traditional next-item prediction, we propose a session-wise generation, which is more elegant and contextually coherent than point-by-point generation that relies on hand-crafted rules to properly combine the generated results. 3) an Iterative Preference Alignment module combined

Author

Deng, Jiaxin; Wang, Shiyao; Cai, Kuo; Ren, Lejian; Hu, Qigen; Ding, Weifeng; Luo, Qiang; Zhou, Guorui

Published

2025

Language

EN