让推荐系统理解动作的上下文含义，提升生成式推荐准确率
ActionPiece: Contextually Tokenizing Action Sequences for Generative Recommendation
- 根据动作上下文动态生成令牌，而非固定匹配
- 通过特征共现频率构建新令牌，提升语义表达力
- 适合需要理解用户行为上下文的推荐场景
生成式推荐（GR）将用户行为序列转化为离散令牌并自回归生成预测。现有模型对每个动作独立分词，相同动作在不同序列中使用相同固定令牌，忽略上下文差异，导致语义失真。为此，我们提出ActionPiece，通过引入上下文信息进行动作分词。每个动作以项目特征集合表示，基于特征模式在单个集合内及相邻集合间的共现频率，构建词汇表。考虑到特征集合无序性，引入集合排列正则化，生成具有相同语义的多种序列分割方式。代码已开源。
原文摘要 · Abstract (English)
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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