參考群體是消費者進行購買決策時之重要參考依據。然而，對消費者而言，現存智慧型手機應用程式之行動平台所提供之排行榜與評價資訊並無法給予個別消費者有效的參考群體資訊做為決策之參考。因此，本研究提出以「顯性評價」為主的「協同過濾式推薦機制」建立新的應用程式排名計算方式，提供個人化的排名服務以減少使用者在行動平台上搜尋其偏好應用程式所浪費的時間。
　　本研究主要分為兩個部分：(1)建立以協同過濾式推薦機制為基礎的個人化應用程式排名系統及(2)評估系統推薦之成效。
　　利用ROC指標評估系統之效能，其結果顯示，本研究所提出之系統利用「協同過濾式推薦機制」建立之個人化排名服務對約70%的目標使用者具有完全正確的預測效果。就整體而言，系統可以對超過90%的使用者縮短其於行動平台搜尋應用程式的時間，平均約可有效地節省約50%的搜尋時間。另外，將使用者已經下載或購買之應用程式排名位置適當地調整更可進一步改善系統的推薦品質。

The reference groups are the important references of customers when they make the purchasing decisions. However, the table of ranking and the evaluated information about applications offered from the mobile platform of smart phone nowadays cannot be supply enough for consumers as the effective reference information to make decisions. Hence, the study proposes a mechanism of collaborative filtering recommender based on the explicit rating to build a new way for computing the applications’ ranking, which can provide the personal ranking service for reducing the time lost in searching preferring applications.
There are two parts of works included in this study. The first part of work is building a personal applications ranking system with the mechanism of the collaborative filtering recommender. The second part of the work is evaluating the recommender effects.
By using the ROC index to evaluate the system efficiency, the experiment results show the totally correct prediction to about 70% of the users. In general, the system can offer users (over 90%) to shorten their time in searching applications on the mobile platform. It can save about 50% time effectively in searching. Besides, properly adjudging the location of the applications, which are downloaded or bought by users, can improve the recommender quality more.

中文文獻
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[9]廖婉淑，「利用整合式的機器學習方式提高垃圾網站偵測率」，國立台灣科技大學資訊工程系，2009。
[10]廖品妍，「以顯性評價為主之相似性推薦」，朝陽科技大學資訊管理學系，2010。
[11]劉睿哲，「Collaborative Filtering based on Co-clustering with CCAM」，國立中央大學資訊工程研究所，2011。
[12]戴英修，「考慮商品異質性改善協同過濾系統」，國立清華大學科技管理研究所，2010。

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