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Chapter 5
Conclusions and Future work
在商務應用上，推薦系統對於商家而言是一個創造額外價值的非常強 大的技術；而另一方面，推薦系統也使用戶受益，因為推薦系統能幫 助找到他們想要購買或者感到興趣的服務或商品項目。若能善用推薦 技術，商家與用戶雙贏的良好局面將被良好建立。
在這份研究中我們將真實資料集應用於 libFM 這個預測工具，實驗 結果驗證了，若能將使用者對於商品或者服務的文字評論經過有效的 整理，使之成為特徵值並帶進推薦系統中進行運算，則推薦系統的效 能將會有顯著提升。
在文字資訊的處理方面，我們採用了詞袋模型以表示使用者的評 論，此一模型的獨立性假設不太符合語言文字實際分布的狀況；近年 來的自然語言處理研究中，語言模型的文件表示法可以處理這類詞彙 相依性的問題，例如 Word to Vector、 Sentence to Vector 等等。未來的 研究或可進一步引用這些新的自然語言處理技術，以完善特徵值的處 理，如此應用於推薦系統，應該會有更好的推薦效能的表現。
此外，在未來的研究中，可嘗試將由學習出來的推薦模型權重參 數做輸出，並對應回原始的語料庫，這部分權重值所代表的是使用者 文字評論的重要性高低，若以這份研究的資料集為例，其中權重較高 者，即是各個城市的用戶評論的代表性詞彙，或可用以做更多質性方 面的探討與研究。
‧ 國
立 政 治 大 學
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N a tio na
l C h engchi U ni ve rs it y
‧ 國
立 政 治 大 學
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N a tio na
l C h engchi U ni ve rs it y
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