隨著智慧型手機的發展與普及，愈來愈多使用者頃向使用智慧型手機來獲取最即時的資訊。這種稱為「微時刻（Micro-Moments）」的使用者行為，通常伴隨著鮮明的使用者偏好、決策條件以及必須要在極短的時間內做出決定。使用者每次拿起手機的平均使用時間約為5分鐘，換句話說，系統必須要很快且精確瞭解使用者的需求，並快速提供合適的資訊。本研究透過聊天機器人建構一個以滿足使用者微時刻需求的互動式情境感知推薦系統，並以推薦餐廳為主題，探討如何獲取使用者的偏好以及當下的情境與意圖，並與推薦演算法結合，產生推薦給使用者。研究結果指出，本研究提出的微時刻推薦系統設計可以有效的獲得使用者偏好與意圖以及有考慮使用者當下意圖的演算法可以幫助使用者更快的找到最合適的餐廳並且是符合使用者的偏好。

More and more users tend to use their smartphones to support their micro-moment decisions. Micro-moments can be regards as an intent-rich moment when preferences and decision priorities are expressed clearly. Furthermore, the average time users spent on one moment is less than 5 minutes and they usually need to make a decision in a short time. The traditional information retrieval might not able to meet users’ need. Hence, the context-aware recommender is one of key solution to meet users’ need. Some studies have point out an interactive recommender design can better elicit user preference and contextual information. The emergence of chatbot which mimics a conversation with a real person has been regarded as an ideal conversational agent to build recommender systems. In this study, we proposed a micro-moments recommender system aims to recommend restaurants based on the combination of user’s long-term and short-term intention and is built on a chatbot. The result shows that the proposed micro-moments recommender system is able to let user find a restaurant at moment with less search effort and higher efficiency and help the user bring out their inner intention to get the best choice of restaurants, which is in line with his/her interest.

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