5、 結論與建議
5.2 建議
本研究目前僅以五百筆案例做為案例庫的基礎，倘若未來能增加更 多不同類別的案例資料或增加蒐集之資料量，將可使得推薦機制更多元 化且精準。未來除了加入新產品資料外，尚可將其它重要屬性做微調，
以因應使用者挑選不同的產品類型，藉此提昇推薦的品質。近年來相機 產品規格差異幅度有逐漸縮小的趨勢，為克服此情況，後續的研究發展 可朝向改善權重取決方面進行著手，以因應使用者不同需求而產生相對 應之權重取決方式。另一方面，建構系統之程式語言及資料庫可採用 JAVA或MATLAB等不同的程式開發語言，資料庫方面則可採用MySQL 或SQL Server等，藉此提升系統的相容性及擴充性。此外，本研究採用案 例式推理法之相似度計算做為研究的演算法，未來可以嘗試不同的相似 度計算方式，如歐幾里德距離( euclidean distance )等，或是嘗試不同權重 的計算公式，如詞頻-倒轉文件頻率( term frequency - inverse document frequency, TF-IDF )等方法，亦或是朝向混合式過濾的推薦系統，用以彌 補單一推薦機制之缺點，但是無論如何前提必須是在能夠維持資訊推薦 的精確度情況之下進行。
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