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arXiv · 2309.13068

UNICON: A unified framework for behavior-based consumer segmentation in e-commerce

Manuel Dibak·Vladimir Vlasov·Nour Karessli·Darya Dedik·Egor Malykh·Jacek Wasilewski·Ton Torres·Ana Peleteiro Ramallo
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Abstract

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.

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Manuel Dibak, Vladimir Vlasov, Nour Karessli, Darya Dedik, Egor Malykh, Jacek Wasilewski, Ton Torres, Ana Peleteiro Ramallo. 2023-09-18. UNICON: A unified framework for behavior-based consumer segmentation in e-commerce. https://arxiv.org/abs/2309.13068

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