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This paper proposes a dual-layer user model to generate descriptive service recommendations for user-adaptive service robots. The user model represents user preferences as the associative memory in the bottom-layer and association rules in the top-layer. The learning and inference processes in the two layers, and the bottom-up rule extraction process, are explained. The proposed user model was applied to a user-adaptive coffee menu recommendation system, and the quantitative and qualitative performances of the user-adaptive and descriptive recommendation system were evaluated by comparison with non-descriptive and random recommendation methods.