Inductive Generative Recommendation via Retrieval-based Speculation

Understand

Generative recommendation (GR) is an emerging paradigm that tokenizes items into discrete tokens and learns to autoregressively generate the next tokens as predictions.

• While this token-generation paradigm is expected to surpass traditional transductive methods, potentially generating new items directly based on semantics, we empirically show that GR models predominantly generate items seen during training and struggle to recommend unseen items.
• 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.