| 摘要: | 為有效塑模及蒐集使用者網路偏好屬性與及時回饋使用者網路之搜尋，於本研究中我們提出一個以智慧型使用者行為回饋之模型。本模型具塑模使用者網路萃取屬性、使用者網路行為分類、評估使用者網路偏好行為等功能，並能減少使用者網路搜尋時間及提升使用者個人偏好行為回饋效率及商品之推薦等。
本研究建構智慧型使用者行為回饋模型，本模型可供使用者網路搜尋資料及時回饋、正確提供訊息至使用者。本模型具有二個子模型：其一為使用者行為蒐集子模型、另一為使用者行為與商品推薦子模型。使用者行為蒐集子模型可透過推薦網站蒐集及萃取使用者網路行為屬性；使用者行為商品與推薦子模型將使用者行為蒐集子模型所蒐集之訊息，經本研究所提之協同過濾演算法進行資訊過濾、商品索引及推薦。透過本模型，可及時提供符合使用者偏好之資訊。
本研究以網頁服務(Web Server)，架構美食推薦網站於雲端平台以Amazon EC2為開發工具架構Hadoop及Mahout進行資料探勘建構本模型。透過本模型實作的結果，可縮短使用者網路搜尋時間並可及時提供符合使用者偏好資訊與商品推薦等。
ABSTRACT
In this study, we propose an intelligent feedback model of users, which can collect the preference features of online users and give online users on-time feedback for search. The proposed model includes three main functions for online users, which are feature extraction, behavior classification and behavior preferences evaluation. Also, it can save the search time for users, make the preference feedback effectively and recommend the commodity for online users.
We build an intelligent user feedback model to provide the on-time feedback and correct information for on-line users. The proposed model consists of two sub-models, namely the User Behavior Collection sub-model and the Recommendation sub-model for User Behavior and Commodity. The user behavior collection sub-model collects and extracts the online user features through recommendation web sites. The recommendation sub-model for user behavior and commodity make the data/information from the first sub-model filtered, indexed and recommended using the proposed collaborative filer algorithm.
We used Web server to host websites and let Amazon EC2 as cloud computing platform to do data mining in Hadoop and Mahout based implementations. As the result shows that our proposed model not only can save the search time for online users, make the preference feedback effectively but also can recommend the commodity on time for users. |
