未來研究方向

在未來的研究方向，建議可朝以下幾個方面進行：

 在產生關聯規則方面，本論文只單純的比較了 WEKA 所提供的 Apriori 演算法來產生規則，因此未來後續可以嘗試別種演算法，並比較在建構貝 氏網路的上是否能獲得更好的成果。

 於鼓勵使用者參與評比部分，未來可加入考慮使用者風險評估取向，包含 代理人是風險中立(risk-neutral)或是風險趨避(risk-averse)。

 雖然本論文實驗所使用之資料集為真實資料，但由於本論文並未於真實的 推薦網站運作，因此在實驗中觀察使用者回評已刪除項目部分，亦受限於 模擬長期的交易狀況。因此未來可將本論文所提之方法運用在真實情境 下，以了解是否也能有如本實驗中之效益。

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參考文獻-中文部份

[C1] 鄭祐昇，「運用資料探勘技術於知識圖之建立」，東海大學，碩士論文，2003。

[C2] 吳思怡，「運用貝氏網路與關聯規則於產品推薦之研究」，輔仁大學，碩士論文，

2006。

[C3] 張維迎、劉楚俊，賽局理論與訊息經濟學，頁數179-220。

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[30] Apache Mahout and taste, http://mahout.apache.org/

[31] Weka, http://www.cs.waikato.ac.nz/ml/weka/

[32] Msbnx, http://research.microsoft.com/en-us/um/redmond/groups/adapt/msbnx/

[33] GroupLens, http://www.grouplens.org/