Although sophisticated sequence modeling paradigms have achieved remarkable success in recommender systems, the information capacity of hand-crafted sequential features constrains the performance upper bound. To better enhance user experience by encoding historical interaction patterns, this paper presents a novel two-stage sequence modeling framework termed Instance-As-Token (IAT). The first stage of IAT compresses all features of each historical interaction instance into a unified instance embedding, which encodes the interaction characteristics in a compact yet informative token. Both temporal-order and user-order compression schemes are proposed, with the latter better aligning with the demands of downstream sequence modeling. The second stage involves the downstream task fetching fixed-length compressed instance tokens via timestamps and adopting standard sequence modeling approaches to learn long-range preferences patterns. Extensive experiments demonstrate that IAT significantly outperforms state-of-the-art methods and exhibits superior in-domain and cross-domain transferability. IAT has been successfully deployed in real-world industrial recommender systems, including e-commerce advertising, shopping mall marketing, and live-streaming e-commerce, delivering substantial improvements in key business metrics.

翻译：尽管复杂的序列建模范 paradigm 已在推荐系统中取得显著成功，但手工构建的序列特征的信息容量仍制约着性能上限。为通过编码历史交互模式更好地提升用户体验，本文提出一种新颖的两阶段序列建模框架——实例即令牌（Instance-As-Token, IAT）。IAT的第一阶段将每个历史交互实例的所有特征压缩为统一的实例嵌入，该嵌入以紧凑且信息丰富的令牌形式编码交互特征。本文提出时序压缩与用户序压缩两种方案，其中后者更契合下游序列建模的需求。第二阶段通过时间戳获取固定长度的压缩实例令牌，并采用标准序列建模方法学习长程偏好模式。大量实验表明，IAT显著优于现有最优方法，并展现出优越的域内与跨域迁移能力。目前IAT已成功部署于电商广告、购物中心营销和直播电商等真实工业推荐系统，在关键业务指标上实现了显著提升。