Inductive Generative Recommendation via Retrieval-based Speculation
<|reference_start|>Inductive Generative Recommendation via Retrieval-based Speculation: Generative recommendation (GR) is an emerging paradigm that tokenizes items into discrete tokens and learns to autoregressively generate the next tokens as predictions. Although effective, GR models operate in a transductive setting, meaning they can only generate items seen during training without applying heuristic re-ranking strategies. In this paper, we propose SpecGR, a plug-and-play framework that enables GR models to recommend new items in an inductive setting. SpecGR uses a drafter model with inductive capability to propose candidate items, which may include both existing items and new items. The GR model then acts as a verifier, accepting or rejecting candidates while retaining its strong ranking capabilities. We further introduce the guided re-drafting technique to make the proposed candidates more aligned with the outputs of generative recommendation models, improving the verification efficiency. We consider two variants for drafting: (1) using an auxiliary drafter model for better flexibility, or (2) leveraging the GR model's own encoder for parameter-efficient self-drafting. Extensive experiments on three real-world datasets demonstrate that SpecGR exhibits both strong inductive recommendation ability and the best overall performance among the compared methods. Our code is available at: https://github.com/Jamesding000/SpecGR.<|reference_end|>
@article{ding2024inductive,
    title={Inductive Generative Recommendation via Retrieval-based Speculation},
    author={Yijie Ding, Yupeng Hou, Jiacheng Li, Julian McAuley},
    journal={arXiv preprint arXiv:2410.02939},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.02939},
    primaryClass={cs.IR}
}