ActionPiece: Contextually Tokenizing Action Sequences for Generative
  Recommendation
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, which serve as the initial tokens. 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. Experiments on public datasets demonstrate
that ActionPiece consistently outperforms existing action tokenization methods,
improving NDCG@10 by 6.00% to 12.82%.
67b6ee04412c9eccae5151f5
Yupeng Hou
claimed_verified
2025-02-20T09:35:14.498Z
64a62c2f500beb50968e5c9c
https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/wfL3ojJmXqyzGUmCblPf4.jpeg
Yupeng Hou
user
hyp1231
67b6ee04412c9eccae5151f6
Jianmo Ni
67b6ee04412c9eccae5151f7
Zhankui He
admin_assigned
2025-02-20T17:40:37.697Z
64daab70c38427829daf5958
https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/cjL27oSvuJf1x0Zq3SrEJ.jpeg
Zhankui He
user
ZhankuiHe
67b6ee04412c9eccae5151f8
Noveen Sachdeva
admin_assigned
2025-02-20T17:40:31.037Z
652e529acb01191873067b02
/avatars/a340d5faca6ad1d120e8a904380276e9.svg
Sachdeva
user
Noveen
67b6ee04412c9eccae5151f9
Wang-Cheng Kang
67b6ee04412c9eccae5151fa
Ed H. Chi
67b6ee04412c9eccae5151fb
Julian McAuley
67b6ee04412c9eccae5151fc
Derek Zhiyuan Cheng
admin_assigned
2025-02-20T17:40:05.814Z
624cedaa43ce7877c5af2c4e
/avatars/86f13f8e082e5c0b8ea671c374e4d675.svg
Zhiyuan Cheng
user
willcee
ActionPiece: Contextually Tokenizing Action Sequences for Generative
  Recommendation
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, which serve as the initial tokens. 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. Experiments on public datasets demonstrate
that ActionPiece consistently outperforms existing action tokenization methods,
improving NDCG@10 by 6.00% to 12.82%.
ActionPiece improves generative recommendation by incorporating contextual features into action tokenization, leading to better performance on public datasets.
autoregressive generation tokenization GR models ActionPiece feature patterns co-occurrence frequency set permutation regularization NDCG@$10$