本研究所提之方法，雖可推薦出符合使用者需求偏好之產品，改善 傳統資訊推薦系統常使用的明確查詢，但研究思緒是無限的，從各種角 度都可運用不同的方法來解決不同的問題。
因此，本研究不夠完善之處，希望有待未來能夠將其補足並加強，
使本研究論文能更臻完整，故以提出未來之研究方向，詳細內容其整理 如下。
（一） 應用領域與資料收集
由於本研究選擇音樂 CD 產品做為應用領域，因時間上的考量與資 料收集，所探討的音樂 CD 資訊推薦之範圍，是以單一網站上的發燒片 音樂資訊為主，故此希望未來可以加入更多類型之音樂 CD 資訊。
（二） 關鍵詞擷取
本研究音樂關鍵詞之擷取工作仍需要仰賴人工的定義，尚未加入規 則推理等判斷之機制來協助辨識音樂關鍵詞，可能影響擷取關鍵詞之精 確度，因此希望未來可發展自動化技術來進行關鍵詞的擷取。
（三） 資訊推薦演算法改進
本 研 究 資 訊 推 薦 演 算 法 部 份 ， 則 採 用 歐 幾 里 德 距 離 （ Euclidean
嘗試採用其他計算距離之演算法，並且能在不失推薦準確度下提高計算 速度。
（四） 朝向混合式推薦機制
混合式推薦機制（Hybrid-based recommendation）可互補其單一推薦 機制之缺失，故期望在未來研究可進一步結合其他推薦機制來發展更臻 完善的資訊推薦系統。
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