第五章 結論與未來可能研究方向
5.2 未來可能研究方向
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5.2 未來可能研究方向
本研究提出的推薦系統的框架目前提供的演算法，未來可以加入不同的 Co-Clustering 演算法，比較各演算法的準確率及召回率。目前我們所使用的資料集為使用者操作手機應 用程式的紀錄，根據此資料集，我們推薦應用程式給使用者，在這樣的架構下，我們可以 套用不同的資料集，將資料集轉換成矩陣資料，即可對使用者做不同項目的推薦，保有彈 性。目前我們使用了兩種推薦方式，分別是推薦同群使用者最多和使用次數或頻率最高的 應用程式，未來也能加入其他的推薦方法，比較各種推薦方法的結果。未來也能將實驗使 用演算法分群之後的結果，使用工具圖形化，可以觀察分群的分布。由本研究實驗所推薦 的應用程式名單，驗證使用者確實有使用的部分，未來我們可以實際找使用者做評比，確 認是否真的喜歡我們所推薦的應用程式。另外，未來我們也可以對使用者進行興趣指標訪 談，針對每位不同使用者的興趣做推薦。在應用程式的部分，未來可以先分成一般用途和 專門用途兩種，之後再分別進行推薦。
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