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To this end, we propose a novel taxonomy-based contextual data augmentation method to enhance the retrieval accuracy. The taxonomy is generated based on a one-month diary study, which collects realistic user queries and the necessary contextual information for integrating with captured memories. The retrieved memories are then used to generate answers to the user queries using a large language model (LLM).

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