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ActionPiece: Contextually Tokenizing Action Sequences for Generative Recommendation

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arxiv 2502.13581 v3 pith:Z45ZXP7E submitted 2025-02-19 cs.IR cs.LG

Yupeng Hou , Jianmo Ni , Zhankui He , Noveen Sachdeva , Wang-Cheng Kang , Ed H. Chi , Julian McAuley , Derek Zhiyuan Cheng This is my paper · ORCID
classification cs.IRcs.LG
keywords actionsequencesactionpiecesamesetsacrossactionsconsidering
verification ladder T0 review T1 audit T2 compute T3 formal
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Generative recommendation (GR) is an emerging paradigm where user actions are tokenized into discrete token patterns and autoregressively generated as predictions. However, existing GR models tokenize each action independently, assigning the same fixed tokens to identical actions across all sequences without considering contextual relationships. This lack of context-awareness can lead to suboptimal performance, as the same action may hold different meanings depending on its surrounding context. To address this issue, we propose ActionPiece to explicitly incorporate context when tokenizing action sequences. In ActionPiece, each action is represented as a set of item features. Given the action sequence corpora, we construct the vocabulary by merging feature patterns as new tokens, based on their co-occurrence frequency both within individual sets and across adjacent sets. Considering the unordered nature of feature sets, we further introduce set permutation regularization, which produces multiple segmentations of action sequences with the same semantics. Our code is available at: https://github.com/google-deepmind/action_piece.

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Forward citations

Cited by 11 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

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