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Building upon the previous chapter, which demonstrated how enriching the UCR approach with temporal information enhances its expressiveness and predictive power, this chapter extends the discussion to another critical challenge, which is relevant for academia and industry alike: the need for continuous adaptability in dynamic e-commerce environments. While TEE addresses the question of how to encode additional session-based behavioral signals, they do not fully resolve the problem of how to maintain and evolve customer representations over time as new products, interactions, and behavioral patterns emerge. Accordingly, this chapter tackles the challenge of adaptability by addressing the third research question of this thesis.

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Lifelong Learning Embeddings for Adaptive Customer Behavior Modeling

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  • 发表日期：

    2026年

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  • Miguel Alves Gomes

摘要

Building upon the previous chapter, which demonstrated how enriching the UCR approach with temporal information enhances its expressiveness and predictive power, this chapter extends the discussion to another critical challenge, which is relevant for academia and industry alike: the need for continuous adaptability in dynamic e-commerce environments. While TEE addresses the question of how to encode additional session-based behavioral signals, they do not fully resolve the problem of how to maintain and evolve customer representations over time as new products, interactions, and behavioral patterns emerge. Accordingly, this chapter tackles the challenge of adaptability by addressing the third research question of this thesis.