群眾智慧最大的優點在於：即使不是高層人員，也能夠很有貢獻。一群擁有不同問題處理能力的人，大家共同集思廣益，就能發揮出比聰明的人更優秀的表現，可見多元性比才智來得更重要。創意的開發和評鑑兩者是相輔相成的，一個創意要成功，除了本身是優良的提案之外，評鑑創意的過程也必須準確，讓好的想法更容易突顯、被看見且得到資助，創意市場即開闢了一個嶄新的途徑。 本研究主要目的在於建立一個結合創意開發和創意評鑑兩階段，可以互動交流的「創意市場」平台，讓參與者在匿名的平等討論環境下，自願性地投入個人想法、資訊，並有效將訊息加以彙整聚集。在創意市場中公開發布創意內容，企圖激發學習效果，以虛擬貨幣交易買賣，進行篩選及評鑑作業，評估創意之優劣。同時提供獎勵誘因，鼓勵更多元、新穎的想法，提升參與交易評鑑之意願和洞察能力，以優化評鑑結果。發揮集體智慧功用，幫助國家、公司企業等單位了解目前的趨勢動態，共同面對未來不確定性、複雜多變的情境，有效開發新商品、服務以及未來發展之方針。 本研究主要根據政大與國寶人壽之產學合作計畫，從旁觀察記錄並透過量化的方式，分析市場平台交易數據和問卷結果，發掘創意市場在新產品和策略開發上之成效表現。結果證實，提供獎勵誘因和匿名機制有助於提升參與者之意願，並激發學習效果。創意市場比公司既有決策產生數量更豐富、內容多元性的創新想法。同時，與常見的遴選方式網路投票做比較，在評鑑之鑑別力和認同度上，創意市場的表現較佳。無論在可行性、創新性、預期效益等方面，創意市場產生結果獲得參與者正面評價，主管級人士亦認同創意市場工具，並認為值得推薦給其他企業或產業。

Ubiquitous e-service is one of the most recent links in the chain of evolution that has characterized the different eras of the internetworking environment. In this dissertation, the notion of ambient e-services is defined to identify a new scope of mobile e-services in an ubiquitous environment, addressing dynamic collective efforts between mobile users, dynamic interactions with ambient environments, the moment of value, and low cost provision. We present an ambient e-services framework characterizing three supporting stacks followed by several ambient e-service applications. We propose an ambient e-service environment that explores the promise of exploitation of the collective wisdom of proximal mobile users. In order to leap the trust barrier for the user to embracing these ubiquitous e-services, we propose an Autonomous Trust Model for exploring collective wisdom in the ubiquitous environment (hereafter termed “U-ATM”) as an instance of ASEM. ASEM (Ambient e-Service Embracing Model) addresses the core elements (of relevance to the integrated concern of trust, reputation and privacy) required for assuring such desired features as convenience, safety, fairness and collaboration for mobile users when they engage with ambient e-services. The U-ATM highlights the distributed peer-to-peer interactions under an ad-hoc network composition. It especially accommodates the dynamic short-lived identity characteristics and lightweight computational capacity of mobile devices. The U-ATM we have developed is based on the ZigBee architecture as a collaborative application in the upper layer of the ubiquitous environment. U-ATM design concepts are elaborated and evaluated. A simulation is conducted. Simulation outcomes for trust decision quality enhancement show significant improvement over traditional designs. U-ATM makes it possible for users to collaborate with the nearby user groups for establishing a reliable and trustworthy interaction environment. It also facilitates and empowers the potential benefits of various ubiquitous e-service applications.

隨著人們習慣的改變，從網路上獲取新知漸漸取代傳統媒體，這也延伸產生許多新的行為。社群標籤是近幾年流行的一種透過使用者標記來分類與詮釋資訊的方式，相較於傳統分類學要求物件被分類到預先定義好的類別，社群標籤則沒有這樣的要求，因此容易因應內容的變動做出調整。 問答型網站是近年來興起的一種個開放性的知識分享平台，例如quora、Stack Overflow、yahoo 奇摩知識+，使用者可以在平台上與網友做問答的互動，在問與答的討論中，結合大眾的經驗與專長，幫助使用者找到滿意的答案，使用單純的問答系統的好處是可以不必在不同且以分類為主的論壇花費時間尋找答案，和在關鍵字搜索中的結果花費時間尋找答案。 本研究希望能針對問答型網站的文件做自動標籤分類，運用標籤推薦技術來幫助使用者能夠更有效率的找到需要的問題，也讓問答平台可以把這些由使用者所產生的大量問題分群歸類。 在研究過程蒐集Stack Exchange問答網站共20638個問題，使用naïve Bayes演算法與文件相似度計算的方式，進行標籤推薦，推薦適合的標籤給新進文件。在研究結果中，推薦標籤的準確率有64.2% 本研究希望透過自動分類標籤，有效地分類問題。幫助使用者有效率的找到需要的問題，也能把這些由使用者所產生的大量問題分群歸類。 / With User's behavior change. User access to new knowledge from the internet instead of from the traditional media. This Change leads to a lot new behavior. Social tagging is popular in recent years through a user tag to classify and annotate information. Unlike traditional taxonomy requiring items are classified into predefined categories, Social tagging is more elastic to adjust through the content change. Q & A Website is the rise in recent years. Like Quora , Stack Overflow , yahoo Knowledge plus. User can interact with other people form this platform , in Q & A discussion, with People's experience and expertise to help the user find a satisfactory answer. This study hopes to build a tag recommendation system for Q & A Website. The recommendation system can help people find the right problem efficiently , and let Q & A platform can put these numerous problems into the right place. We collect 20,638 questions from Stack Exchange. Use naïve Bayes algorithm and document similarity calculation to recommend tag for the new document. The result of the evaluation show we can effectively recommend relevant tags for the new question.