Data-driven personalization is a key practice in fashion e-commerce, improving the way businesses serve their consumers needs with more relevant content. While hyper-personalization offers highly targeted experiences to each consumer, it requires a significant amount of private data to create an individualized journey. To alleviate this, group-based personalization provides a moderate level of personalization built on broader common preferences of a consumer segment, while still being able to personalize the results. We introduce UNICON, a unified deep learning consumer segmentation framework that leverages rich consumer behavior data to learn long-term latent representations and utilizes them to extract two pivotal types of segmentation catering various personalization use-cases: lookalike, expanding a predefined target seed segment with consumers of similar behavior, and data-driven, revealing non-obvious consumer segments with similar affinities. We demonstrate through extensive experimentation our framework effectiveness in fashion to identify lookalike Designer audience and data-driven style segments. Furthermore, we present experiments that showcase how segment information can be incorporated in a hybrid recommender system combining hyper and group-based personalization to exploit the advantages of both alternatives and provide improvements on consumer experience.

翻译：数据驱动的个性化是时尚电商的关键实践，通过提供更相关的内容来优化企业服务消费者需求的方式。虽然超个性化能为每位消费者提供高度定向的体验，但需要大量隐私数据来创建个性化旅程。为缓解这一问题，基于群体的个性化通过构建在消费者分群更广泛共同偏好之上的适度个性化，同时仍能实现结果个性化。我们提出UNICON，一种统一的深度学习消费者分群框架，该框架利用丰富的消费者行为数据学习长期潜在表征，并基于这些表征提取两类适应各类个性化场景的关键分群：相似人群扩展（lookalike），通过行为相似的消费者扩展预定义目标种子群体；以及数据驱动分群，揭示具有相似偏好但非显性的消费者群体。通过大量实验，我们证明了该框架在时尚领域识别设计师风格相似人群及数据驱动风格分群的有效性。此外，我们通过实验展示了如何将分群信息整合到结合超个性化与群体个性化的混合推荐系统中，以发挥两种方案的协同优势，提升消费者体验。