融合线上线下行为，提升推荐系统对用户未来互动的预测能力。
Improving Sequential Recommender Systems with Online and In-store User Behavior
- 构建跨渠道数据管道，统一整合线上浏览与线下购物数据。
- 引入模型无关编码器，增强对线下交易行为的建模能力。
- 适合需融合多场景用户行为的电商推荐系统开发者。
在线电商正拓展至线下购物场景，用户可在享受传统线上浏览与结账体验的同时探索实体店铺。然而，线上与线下行为的频繁切换给未来在线互动预测带来了挑战，根源在于缺乏对混合用户行为（线上与线下）的全面建模。挑战有二：其一，需设计新数据管道，将线上与线下用户行为数据整合到统一数据结构中，并支持预训练、训练、推理等模型生命周期阶段的无缝衔接；其二，仅依赖线上行为序列的推荐系统必须重构，以在序列建模框架下兼容线上与线下数据输入。为此，我们提出一种混合式全渠道数据管道，通过缓存多元数据源信息，实现线上线下行为数据的整合。随后，引入一个模型无关的编码模块，用于解析用户线下交易行为，增强推荐系统对混合行为的建模能力，从而更精准地预测用户的后续在线交互。
原文摘要 · Abstract (English)
Online e-commerce platforms have been extending in-store shopping, which allows users to keep the canonical online browsing and checkout experience while exploring in-store shopping. However, the growing transition between online and in-store becomes a challenge to sequential recommender systems for future online interaction prediction due to the lack of holistic modeling of hybrid user behaviors (online and in-store). The challenges are twofold. First, combining online and in-store user behavior data into a single data schema and supporting multiple stages in the model life cycle (pre-training, training, inference, etc.) organically needs a new data pipeline design. Second, online recommender systems, which solely rely on online user behavior sequences, must be redesigned to support online and in-store user data as input under the sequential modeling setting. To overcome the first challenge, we propose a hybrid, omnichannel data pipeline to compile online and in-store user behavior data by caching information from diverse data sources. Later, we introduce a model-agnostic encoder module to the sequential recommender system to interpret the user in-store transaction and augment the modeling capacity for better online interaction prediction given the hybrid user behavior.
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