title: ActionPiece: Contextually Tokenizing Action Sequences for Generative
    Recommendation
summary: 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%.
paper_authors._id: 67b6ee04412c9eccae5151f5
paper_authors.name: Yupeng Hou
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-02-20T09:35:14.498Z
paper_authors._id: 64a62c2f500beb50968e5c9c
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/wfL3ojJmXqyzGUmCblPf4.jpeg
paper_authors.fullname: Yupeng Hou
paper_authors.type: user
paper_authors.user: hyp1231
paper_authors._id: 67b6ee04412c9eccae5151f6
paper_authors.name: Jianmo Ni
paper_authors._id: 67b6ee04412c9eccae5151f7
paper_authors.name: Zhankui He
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-02-20T17:40:37.697Z
paper_authors._id: 64daab70c38427829daf5958
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/cjL27oSvuJf1x0Zq3SrEJ.jpeg
paper_authors.fullname: Zhankui He
paper_authors.type: user
paper_authors.user: ZhankuiHe
paper_authors._id: 67b6ee04412c9eccae5151f8
paper_authors.name: Noveen Sachdeva
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-02-20T17:40:31.037Z
paper_authors._id: 652e529acb01191873067b02
paper_authors.avatarUrl: /avatars/a340d5faca6ad1d120e8a904380276e9.svg
paper_authors.fullname: Sachdeva
paper_authors.type: user
paper_authors.user: Noveen
paper_authors._id: 67b6ee04412c9eccae5151f9
paper_authors.name: Wang-Cheng Kang
paper_authors._id: 67b6ee04412c9eccae5151fa
paper_authors.name: Ed H. Chi
paper_authors._id: 67b6ee04412c9eccae5151fb
paper_authors.name: Julian McAuley
paper_authors._id: 67b6ee04412c9eccae5151fc
paper_authors.name: Derek Zhiyuan Cheng
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-02-20T17:40:05.814Z
paper_authors._id: 624cedaa43ce7877c5af2c4e
paper_authors.avatarUrl: /avatars/86f13f8e082e5c0b8ea671c374e4d675.svg
paper_authors.fullname: Zhiyuan Cheng
paper_authors.type: user
paper_authors.user: willcee
paper_title: ActionPiece: Contextually Tokenizing Action Sequences for Generative
    Recommendation
paper_summary: 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%.
paper_ai_summary: ActionPiece improves generative recommendation by incorporating contextual features into action tokenization, leading to better performance on public datasets.
paper_ai_keywords: autoregressive generation tokenization GR models ActionPiece feature patterns co-occurrence frequency set permutation regularization NDCG@$10$