Traditional user modeling (UM) approaches have primarily focused on designing
models for a single specific task, but they face limitations in generalization
and adaptability across various tasks. Recognizing these challenges, recent
studies have shifted towards continual learning (CL)-based universal user
representation learning aiming to develop a single model capable of handling
multiple tasks. Despite advancements, existing methods are in fact evaluated
under an unrealistic scenario that does not consider the passage of time as
tasks progress, which overlooks newly emerged items that may change the item
distribution of previous tasks. In diesem Papier, we introduce a practical
evaluation scenario on which CL-based universal user representation learning
approaches should be evaluated, which takes into account the passage of time as
tasks progress. Then, we propose a novel framework Dynamic Time-aware continual
user representation learner, named DITTO, designed to alleviate catastrophic
forgetting despite continuous shifts in item distribution, while also allowing
the knowledge acquired from previous tasks to adapt to the current shifted item
distribution. Through our extensive experiments, we demonstrate the superiority
of DITTO over state-of-the-art methods under a practical evaluation scenario.
Our source code is available at
https://github.com/seungyoon-Choi/DITTO_official.

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