ActionPiece: Contextually Tokenizing Action Sequences for Generative
  Recommendation
Authors: name: Yupeng Hou, name: Jianmo Ni, name: Zhankui He, name: Noveen Sachdeva, name: Wang-Cheng Kang, name: Ed H. Chi, name: Julian McAuley, name: Derek Zhiyuan Cheng

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%.