鑑於國人網路依賴度越來越高，每人每日透過網路獲取豐富的資訊及資料，然而由於資訊過多也造成使用者不知道自己真正想要的資訊，因此找尋出個人化資訊勢必成為未來研究的方向。一般網路行銷廣告都是在分析使用者在網站上的行為，然而鮮少去分析到使用者端環境及儲存的資料。相較於使用者在網站上的行為，使用者端所留下的行為資訊更能夠反應出使用者的真正興趣，因此本研究主要探討在個人使用環境(如：PC、NB)中，透過使用者最常接觸的三種途徑(包括：上網瀏覽資訊、信件資訊及常用檔案內容資訊)來獲取使用者興趣的資訊，並且建構出一套個人化廣告推薦系統常駐於使用者端即時(real-time)記錄使用者的行為資訊。本系統運用推薦技術(Recommendation Technique)搭配關聯式法則(Association Rule)來將這些資訊有效的過濾並關聯出使用者的喜好及興趣，同時利用這些資訊上網找尋合適的廣告資料，用以建構出個人化廣告推薦模式。 / The rapid growth of Internet has changed the patterns of our life. Everyone can gain rich information on internet, but plenty of information will confuse user's ability to determine whether these information are useful. Therefore, the further trend is to discover personalized information. Many researches about internet marketing advertisement are mining user's behavior in website server, but scarce researches focus on client. Compared to user's behavior in website, the behavior information which stays in local environment (ex. PC, NB) can reflect user's profile more. Thus, this research mainly discuss how to record user's behavior information containing Web Page Title, E-mail Subject and Document Content in local environment and how to construct a personalized advertisement recommendation system resident in memory of local environment for timely (On-line) collecting user's behavior information to create “user profile” using recommendation technique and association rule. This system will utilize “user profile” to provide appropriate personalized advertisement for user. Finally, we apply several experiments to verify the feasibility of our system.

近年來，推薦系統（recommendation system）相關研究是一個很熱門的議題，當使用者看到一篇文章，對該文章所描述的事件很感興趣，想要了解該事件的全貌，此時想要得到是該事件的通盤的見解，而非局部的意見，也就是以不同角度去解析此事件的文章清單時，若以過去傳統推薦系統的作法，推薦與這篇文章相似的文章給使用者就未必合適，因為相似文章只能反映對此事件相同角度，而非對此事件不同角度的文章。因此，本研究擬使用社群性標籤（social tag）解決以上問題。透過不同使用者標註標籤反映不同看法的機制，我們可以從文章中選出代表性的標籤，透過該標籤組與文章分數計算，找出對此事件不同角度的文章清單推薦給使用者。實驗結果顯示，若文章有較高的可信度擁有多種角度，則使用我們提出的演算法確實擁有較好的準確度。

協同過濾是電子商務中最常被使用也是最成功的推薦技術，但隨著電子商務的發展，網站使用者與商品數也迅速成長，使得使用者相關資料稀疏(Data sparsity)而嚴重影響推薦品質。對於新使用者與新商品，協同過濾也無法提供準確的推薦。為改善以上問題，本研究使用Lemire與Maclachlan (2005)所提出的Slope One演算架構及資料探勘方法中的單純貝式分類器(Naïve bayes classifier)來解決資料稀疏性和冷開始(Cold-start)問題。同時，考量到運算成本，將推薦系統架構分為離線預處理階段和線上預測階段，以避免當使用者數目和商品越來越大時運算成本超過實際可接受程度。 本研究採用MovieLens資料庫的資料集，包含943位使用者與1,682部電影，共10萬筆評比資料，評比分數範圍從1到5分，其中每位使用者至少評比20部以上電影。實驗評估方法則採用平均絕對誤差(MAE)來計算本研究的推薦系統對消費者喜好預測的準確度。 本研究希望所提出的個人化推薦系統能改善傳統協同過濾推薦系統的推薦品質，減少資料稀疏所造成的推薦誤差，更準確的推薦使用者感興趣的物品，以幫助使用者更有效率的進行線上消費，提高顧客滿意度與忠誠度，也提升電子商務網站營業效益。

在現在的社會裡，手機應用程式已經被人們接受與廣泛地利用，然而目前市面上的手機 App 推薦系統，多以使用者實際使用與回報作為參考，若有惡意行為軟體，在使用者介面後竊取使用者資料，這些推薦系統是難以查知其行為的，因此我們提出了 AppReco，一套可以系統化的推薦 iOS App 的推薦系統，而且不需要使用者去實際操作、執行 App。 整個分析流程包括三個步驟：(1) 透過無監督式學習法的隱含狄利克雷分布(Latent Dirichlet Allocation, LDA)做出主題模型，再使用增長層級式自我組織映射圖(Growing Hierarchical Self-Organizing Map, GHSOM)進行分群。(2)使用靜態分析程式碼，去找出其應用程式所執行的行為。(3)透過我們的評分公式對於這些 App，進行評分。 在分群 App 方面，AppReco 使用這些應用程式的官方敘述來進行分群，讓擁有類似屬性的手機應用程式群聚在一起；在檢視 App 方面，AppReco 透過靜態分析這些 App 的程式碼，來計算其使用行為的多寡；在推薦 App 方面，AppReco 分析類似屬性的 App 與其執行的行為，最後推薦使用者使用較少敏感行為(如使用廣告、使用個人資料、使用社群軟體開發包等)的 App。 而本研究使用在 Apple App Store 上面數千個在各個類別中的前兩百名 App 做為我們的實驗資料集來進行實驗。 / Mobile applications have been widely used in life and become dominant software applications nowadays. However there are lack of systematic recommendation systems that can be leveraged in advance without users’ evaluations. We present AppReco, a systematic recommendation system of iOS mobile applications that can evaluate mobile applications without executions. AppReco evaluates apps that have similar interests with static binary analysis, revealing their behaviors according to the embedded functions in the executable. The analysis consists of three stages: (1) unsupervised learning on app descriptions with Latent Dirichlet Allocation for topic discovery and Growing Hierarchical Self-organizing Maps for hierarchical clustering, (2) static binary analysis on executables to discover embedded system calls and (3) ranking common-topic applications from their matched behavior patterns. To find apps that have similar interests, AppReco discovers (unsupervised) topics in official descriptions and clusters apps that have common topics as similar-interest apps. To evaluate apps, AppReco adopts static binary analysis on their executables to count invoked system calls and reveal embedded functions. To recommend apps, AppReco analyzes similar-interest apps with their behaviors of executables, and recommend apps that have less sensitive behaviors such as commercial advertisements, privacy information access, and internet connections, to users. We report our analysis against thousands of iOS apps in the Apple app store including most of the listed top 200 applications in each category.

選擇到哪家餐廳用餐的問題，不論旅遊或家居都經常會遇到。大多數的人會先上網，尋找符合自己喜好且評價好的美食。然而網際網路發達，在人人都可上網分享的情況下，造成資訊氾濫超載。使得使用者上網瀏覽資料時，很容易找到不切合需求的資訊。解決此資訊超載的方法之一是餐廳推薦系統。儘管目前有很多的推薦應用程式或是分享平台，諸如TripAdvisor、iPeen愛評網、foursquare…等等，資料豐富但卻沒有針對個人偏好做推薦。 　　本研究有鑒於許多人在品嘗美食之前，會先拍照並在Facebook或Instagram打卡做紀錄、分享給朋友，打卡的次數可能意味著此餐廳的熱門度。且使用者選擇的美食類型偏好也可能受到聚餐目的的影響。因此開發出一款結合社群互動圈以及考量用餐情境的餐廳推薦系統。此系統先利用使用者所選擇的聚餐場合、價位、餐廳類型、熱門商圈等元素篩選出合適的餐廳，再利用Facebook打卡資料取得與使用者偏好相似的好友，依據好友的相似度推算出使用者對餐廳的喜好程度，推薦符合使用者興趣及需求的餐廳，協助使用者能夠更容易地找到自己所喜好的店家。 　　本研究的實作系統，經過評估測試，結果發現結合社群互動圈及考量用餐情境的個人化推薦能讓使用者更容易找到自己所喜好的餐廳，而在推薦內容中顯示好友對餐廳的評論，更有效的幫助使用者作決策。未來本推薦系統所使用之結合情境元素所設計的模式亦可應用至其他領域的推薦平台，如旅遊景點推薦或旅遊住宿推薦。 / Most people face the issue of deciding which restaurant to eat. Searching through the Internet is the first step that people usually do. However the rapid growth of information has overloaded the Internet users, it makes difficult to find the most appropriate information for decision-making. Certainly there are several restaurant recommender systems have been developed to solve the problem, such as TripAdvisor, iPeen, foursquare, etc; but few systems provide personalized and context-based recommendations. 　　The research intends to develop a restaurant recommender system that considers the factors of social network and context. Nowadays, when people eat, they like to take a picture and check in on Facebook or Instagram to share with friends, the numbers of check-in for a restaurant may mean the restaurant’s popularity. In addition, the gathering purpose and personal preferences may also affect the users’ decisions. Therefore the recommender system first used the variables of eating criteria such as place, price, types of food, eating environments to filter restaurants. The system then got the user’s similar friends from check-in data of Facebook. Through calculating friends’ similarity and their preference of restaurants, the recommender system finds the most fitted ones for the user to choose from. 　　The afterward system’s users testing data prove that this personalized and context-based recommendation system provides better information to help the user make their decisions. The same model can be replicated to other domain of recommender platforms.

近年來電子商務蓬勃發展，嚴重侵蝕實體通路業績，因此線下服務提供者更應善用資料科學技術，找出顧客未被滿足之需求，進而提供優質服務，其中脫穎而出的關鍵非推薦系統莫屬。 本研究以運用計算產品相似程度的「項目導向協同過濾」和計算使用者與商品蘊含特徵的「潛在因子」兩大類「協同過濾」推薦方法為核心，藉由實體零售通路累積的顧客消費紀錄，驗證「協同過濾」方法較傳統熱門商品推薦機制更符合消費者偏好，且「協同過濾」方法能達到完全個人化推薦之目標。 本研究使用的實體零售通路消費紀錄源於顧客真實購物行為，收集成本低，且數據量龐大，然而此類資料無法直接傳達顧客對於商品的喜好與滿足程度，因此被稱之為「內隱資料」，針對內隱資料處理上，本研究選擇以消費次數取代金額，提出短期重複行為計算閾值概念，以時間修正權重處理可能的偏好轉變與習慣性消費。 模型評估方面，透過強調推薦順序的「平均排名百分比」作為指標，利用傳統熱門商品推薦為基準，比較「項目導向協同過濾」和「潛在因子」兩大類「協同過濾」方法推薦品質的優劣，本研究顯示兩大類「協同過濾」方法達到的推薦品質皆優於熱門商品推薦，且前者遞交的推薦清單為完全個人化，運用本研究發展的推薦系統，將其導入與應用，讓線下服務提供者在與每位顧客接觸的關鍵時刻，能在洞悉對方需求的利基上，提供令顧客滿意的商品與服務，創造獨特且難以模仿的競爭優勢。

在目前網路和資訊科技迅速發展的環境底下，我們可對推薦系統做多方面的應用，而我們可知在一般家庭底下或多或少都會面臨一些狀況。為瞭解決大多數家庭都會面臨到的問題並省卻一般以人工來解決問題的方式，我們將提供一套線上解答系統並適時推薦有效答案給使用者。首先可利用FAQ的機制，讓使用者自行在系統上面尋找解決的方法和管道，提供一般的基本解答；然後結合推薦系統的功能，讓此問題的解答更加個人化。有別於一般的推薦系統只以個人為推薦單位，此系統將以家庭為單位的基礎底下實現推薦機制，以期能夠適時給予屬於整個家庭為核心之最適當的建議。並藉由使用者本身對系統之解答所做出的回饋來持續修正系統的準確度，以提高使用者的滿意度。 / Under current E-business environment, it is a great issue to keep customer's relationship by improving their satisfaction. On the other hand, it is usual to have some parent-children problems in families. Therefore, in order to assist to the problem-solving that most families would encounter, this research proposes an on-line system to recommend answers to users in appropriate time. The system would combine the mechanism of FAQ, which offer the general basic answer, and the function of recommender system to allow the personalized answers further. The proposed system is different from other recommender systems because we take the whole family as a unit, not just the looking-for-help parent or child. In addition, we would collect feedbacks provided by users who have applied the system answers. It is hoped to improve the user’s satisfactions, solve their family problems.

在人們的日常生活中，推薦是很普遍的一種社會行為，它使人們不必親自去體驗所有的事物，可透過別人的經驗來得知一件事情或商品的好或壞。隨著科技的快速發展與網際網路的普及，電子商務已逐漸的融入社會，成為人類生活中不可或缺的一部分。然而在網路上過量的資訊，使得個人在資訊的使用與搜尋上面臨極大的挑戰，更加刺激了對於推薦資訊的需求，因此許多推薦技術相繼提出，推薦系統也應運而生，不僅使得推薦的範圍擴大了，推薦的型態也更為豐富多元；同時，在近年電子商務的發展中，對於個人化與顧客導向服務的愈益重視，使得推薦系統逐漸成為一種必要的線上服務。 在眾多的推薦技術之中，協同過濾推薦方法是最成功且最常被採用的推薦技術之一，許多台灣的拍賣平台上也都有採用類似概念的推薦系統，像是Yahoo!拍賣、露天拍賣上的評價機制均屬此類。然而，現行的拍賣評價機制都沒有採用社會網路的技術，本研究希望透過協同過濾與社會網路的結合，讓評價機制更趨於完備。 本研究以台灣最大的拍賣網站Yahoo!為例，蒐集了44萬筆交易記錄，並以推薦網(ReferralWeb)系統的矩陣方法為基礎，找出人與商品的關係、商品與類別的關係、人與人的關係，建立起一個社會網路，讓使用者可查詢特定領域的專家，並與之交易。除此之外，也可直接詢問專家關於商品的資訊或購買技巧。透過這樣的機制，希望能降低消費者在購買商品時所產生的交易糾紛，讓人們在網路上的購物體驗能變得更好。 / Nowadays, recommendation is a common social behavior between people. People can evaluate things or commodities from others’ experience and opinions instead of their own experiences. Along with the development of technology and Internet today, E-commerce has become an indispensable part of human life. However, due to the overloaded information, people face a fantastic challenge when accessing and searching on the Internet. Therefore, many methods of recommendation were proposed, and systems of recommendation are to come with the tide of fashion. In addition, the development of E-commerce emphasized on personalization and customer-oriented services more in recent years, which make recommendation system becomes a necessary on-line service gradually. Collaborative Filtering is the most successful and adopted one in numerous recommendation methods. There are many auction platforms in Taiwan also use recommendation systems, such like "Yahoo Auction", "Ruten Auction", etc. However, the previous mentioned recommendation mechanisms haven’t used Social Network technology; this study will propose an recommendation system which combines Collaborative Filtering and Social Network technology. This research collects 440,000 transaction data from the Yahoo auction platform, which is the biggest auction website in Taiwan. Based on the matrix method of ReferralWeb system(Shah, 1997), this research would like to build up the matrix of relationships between Person-Commodity, Commodity-Category, and Person-Person. Based on the three matrixes, finally builds up a Social Network. In the Social Network, users can enquire experts refer to the specific category of commodity, and then refer to the shops which the experts like or directly ask them the commodity information and purchase skill. Relying on the mechanism proposed by this research, our goals are to reduce the transaction disputes arising from consumers purchase commodities, and to let people have better experiences in on-line shopping.

近年來，由於網際網路的興起，網際網路已成為新聞媒體重要的傳播管道之一，許多新聞網站如雨後春筍般的成立，而讀者也樂於使用這類更加便利、高互動性的新聞網站。但是媒體使用網路作為傳播管道，同時也面臨在傳統傳播模式所未遭遇的新挑戰，網路新聞媒體被迫需要創造獨特的內容吸引使用者，也需發展具黏性的社群經營服務，才能與其他具有類似社群互動機制的Web 2.0網站一較長短，留住廣大的使用者群。 本研究嘗試利用新聞為日常生活人們獲得資訊不可或缺管道的獨特優勢，提出一套有效利用新聞使用社群集體智慧(Collective Intelligence）機制，能夠自動化依據使用者顯隱性回饋，針對每篇新聞分析出分歧度、熱門度、話題性三個社群資訊，並以上述三個社群資訊挑選出合適的焦點新聞，以此促進新聞社群使用者對於焦點新聞的討論與互動，進而提昇新聞傳播的效益與新聞社群的凝聚力。實驗結果證實，本研究所提出的機制確實能夠探勘出滿足大多數使用者關注焦點新聞資訊的需求，並且對於輔助記者掌握讀者對於新聞資訊需求及促進新聞社群經營方面都有很大的助益。 / In recent years, due to the rise of the information and communication technologies, the internet has become one of most important communication channel for Journalism. A long with drastically flourished on-line Journalism, models of readers’ information reception changed while they are enjoyed more convenient and interactive websites providing instant information. At the same time, while mass media utilize internet as communication channel, it has also brought unprecedented challenge to traditional communication. On-line Journalism has not only need to create unique content (information) to attract readers; but it also need to develop a more engaging community management services to interact with other communities with similar mechanisms of Web 2.0 sites to retain user’s attention. This study attempts to exam the proposed on-line journalism system for University Press community, which could automatically analyze readers’ community dataset of University newspaper; including opinion deviation indicators, popularity indicator, and topicality indicator of each news (information). This system selects targeted news (information) according to above indicators to promote discussion and interactivity within readers’ community in hope to promote efficiency of news (information) communication and engagement within readers’ community. Experiment results reveal this proposed mechanism could satisfy most readers’ need for headline news; as well as assist Journalists’ understanding on their readers’ need while promoting on-line journalism social networking management.

近來，旅遊資訊廣被旅遊者在網路上使用。雖然網路上的資訊十分豐富，但是使用者仍常常難以找尋到精準的資訊。而旅遊商品的特性為無形的，所以使用者不能實際地來評估這個服務直到他實際地體驗之後。也就是因為此種特性，所以如何讓使用者在真正體驗到之前能夠取得可信與真實的旅遊資訊變得十分重要。為了解決此問題，語意網絡的概念即出現來解決人與電腦間溝通的問題。而一個本體即是由一個正式化的、某一具有精確規格概念的領域來提供之可實行的平台來發展可信的旅遊資訊服務。 在本論文中，我們探討了旅遊推薦系統的發展、其遭遇的問題、語意網相關之技術包含了：網路本體語言、資源描述架構、和一些目前現有的旅遊本體發展的情況。此外，為了要能提供更智慧化的旅遊行程規劃推薦服務，我們將語意的想法帶入了此領域。我們會提出一個方法讓智慧型旅遊行程推薦服務能在本體論的基礎上實現。所以，一系列的旅遊本體會被建構發展，來讓我們的芻形系統能夠做出行程推薦的服務。此提出的系統能夠驗證語意網的概念在旅遊推薦領域的可行性。它亦能利用屬性與之間的關係來推薦出更智慧型的資訊，找出個人化的景點、活動與行程給旅行者。 / Nowadays, travel information is increasing to appeal the tourists on the web. Al- though there are numerous information provided on the web, the user gets puzzled in nding accurate information. The tourism product has an intangible nature in that cus- tomers cannot physically evaluate the services on oer until practically experienced. This makes access to credible and authentic information about tourism products before the actual experience very valuable. In order to solve these problems, the concept of seman- tic web comes into existence to have communication between human and computer. An Ontology being a formal, explicit specication of concepts of a domain provides a viable platform for the development of credible tourism information services. In this paper, we discuss the development of travel recommendation system, the problems it encounters, the related technology about semantic web including OWL, RD- F/RDFS, and some current circumstances of the existing tourism ontologies as well. Futhermore, in order to make more intelligent travel planning recommendation services, we bring the idea of semantic into tourism domain. We will present an approach aimed at enabling intelligent recommendation services in tourism support systems using ontolo- gies. A suite of tourism ontologies was developed and engaged to enable a prototypical tourism system with recommednation capabilities. The proposed system can verify the feasibility and concept of taking semantic web technology into tourism recommendation systems domain. It also can recommend more intelligent information using properties, relationships of travel ontology, and is responsible for nding personalized attractions, activities and a trip itinerary for travelers.