本論文研究活動意圖類型預測方法，提出以相關研究[13]提出的遞迴類神經 網路架構為基礎，以GPS 軌跡資料擷取更多特徵作為模型輸入，並改良模型架構。
本論文提出兩種分群方法，以使用者分群法建立的群組模型，實驗結果顯示在群 組資料充足時，能提升預測結果的Accuracy@1。以序列分群法建立的群組模型則 因為資料不充足，導致效果不明顯。不過經由實驗觀察，透過抽樣與平均群組資 料量差不多的全體資料進行模型訓練，可驗證出以序列分群法增進群組模型預測 效果是有幫助的。此外，本論文提出兩個方式組合全體資料模型和群組模型。實 驗評估顯示，遷移學習模型在 OSM 資料集的預測結果優於全體資料模型和群組 模型，合成模型則是透過調和係數學習結合兩個模型輸出結果的比重，能有效幫 助系統更正確預測出使用者活動意圖。本論文所提的組合模型中，合成模型比遷 移學習模型更能達到本論文所期望之目標，綜合全體資料模型和群組模型結果，
提升對各群組的預測準確率。
本研究未來可進一步根據活動意圖類型，搜索使用者附近符合的 POI，從這 些POI 中推薦地點或相關資料給使用者。
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