SITA：面向长序列推荐的目标感知压缩语义兴趣令牌
SITA: Semantic Interest Tokens for Target-Aware Compression in Long-Sequence Recommendation
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中文总结 AI 辅助
SITA是面向长序列推荐的目标感知压缩框架，通过并行语义量化实现目标感知压缩，在保持可扩展性的同时优于代表性基线，具有实际应用潜力。
中文摘要 AI 辅助
随着现代互联网平台上用户行为历史不断增长，有效建模长行为序列对预测用户对候选物品的兴趣变得至关重要。现有方法沿两个方向发展：一类从长历史中动态检索与目标相关的行为，实现目标感知建模，但推理时需要依赖目标的计算；另一类将整个行为序列压缩为紧凑的用户表示，实现了高效率和可扩展性，但由于编码与目标无关，牺牲了目标特定的适应性。因此关键挑战在于实现目标感知建模，同时保留压缩用户表示的效率和可扩展性。为应对该挑战，我们提出SITA，一种面向长序列推荐的目标感知压缩框架。SITA通过并行语义量化学习到的语义标识符，将压缩后的兴趣组织成语义结构，从而实现目标感知压缩。基于目标物品的语义标识符，SITA自适应聚合相应的结构化兴趣，构建目标特定的用户表示。在公开数据集和大规模工业数据集上的大量实验表明，SITA在保持强可扩展性的同时，始终优于代表性基线，凸显了其在现实推荐系统中的巨大潜力。
英文摘要
As user behavior histories continue to grow on modern Internet platforms, effectively modeling long behavior sequences has become crucial for predicting user interests in candidate items. Existing methods have evolved along two directions. One line dynamically retrieves target-relevant behaviors from long histories, enabling target-aware modeling but requiring target-dependent computation during inference. The other line compresses entire behavior sequences into compact user representations, achieving high efficiency and scalability but sacrificing target-specific adaptation due to target-independent encoding. The key challenge is therefore to enable target-aware modeling while preserving the efficiency and scalability of compressed user representations. To address this challenge, we propose \textbf{SITA}, a target-aware compression framework for long-sequence recommendation. SITA enables target-aware compression by organizing compressed interests into semantic structures through semantic identifiers learned via parallel semantic quantization. Conditioned on the semantic identifier of the target item, SITA adaptively aggregates the corresponding structured interests to construct the target-specific user representation. Extensive experiments on public datasets and a large-scale industrial dataset demonstrate that SITA consistently outperforms representative baselines while maintaining strong scalability, highlighting its strong potential for real-world recommender systems.
