Expert software developer recommendation is a mature research field with many different techniques being developed to help automate the search for experts to help with development tasks and questions. But all previous research on recommending expert developers has had two constant restrictions. First, all previous expert recommendation work assumed that developers only demonstrate positive expertise. But developers can also make mistakes and demonstrate negative expertise, referred to as inexpertise, and show which concepts they don't know as well. Previous research on developer expertise hasn't taken inexpertise into account. Another restriction is that all previous expert developer recommendation research has focused on recommending developers for a single development task or expertise need, such as fixing a bug report or helping with a change request. But not all expertise needs can be easily classified into one of these groups, and having different techniques for every possible task type would be difficult and confusing to maintain and use. We find that inexpertise exists, can be measured, and that it can be used to direct inspection effort to find potentially incorrect or buggy commits. Additionally we investigate how different expertise finding techniques perform on a diverse set of long and short expertise queries and develop new techniques that can get more consistent cross query performance. / Master of Science / Expert software developers are a useful source of information. There have been many papers that research techniques for recommending expert developers for different tasks and questions. But all previous research on recommending expert developers has had two constant restrictions. First, all previous expert recommendation work assumed that developers only demonstrate positive expertise. But developers can also make mistakes and demonstrate negative expertise, referred to as inexpertise, and show which concepts they don’t know as well. Another restriction is that all previous work on recommending expert developers has focused on recommending developers for a single development task or question. But not all expertise needs can be easily classified into one of these groups, and having different techniques for every possible task type would be difficult and confusing to maintain and use. In our first chapter we show that inexpertise exists, can be measured, and that it can be used to help identify potentially buggy or incorrect code. In the second chapter we investigate how different techniques for finding expert developers perform when evaluated on different kinds of expertise finding tasks to find which technique works well on multiples types of tasks.

Nowadays people and organizations are more and more interconnected in the forms of social networks: the nodes are social entities and the links are various relationships among them. The social network theory and the methods of social network analysis (SNA) are being increasingly used to study such real-world networks in order to support knowledge management and decision making in organizations. However, most existing social network studies focus on the static topologies of networks. The dynamic network link formation process is largely ignored. This dissertation is devoted to study such dynamic network formation process to support knowledge management and decision making in networked environments. Three challenges remain to be addressed in modeling and analyzing the dynamic network link formation processes. The first challenge is about modeling the network topological changes using longitudinal network data. The second challenge is concerned with examining factors that influence formation of links among individuals in networks. The third challenge is regarding link prediction in evolving social networks. This dissertation presents four essays that address these challenges in various knowledge management domains. The first essay studies the topological changes of a major international terrorist network over a 14-year period. In addition, this paper used a simulation approach to examine this network's vulnerability to random failures, targeted attacks, and real world authorities' counterattacks. The second essay and third essay focuses on examining determinants that significantly influence the link formation processes in social networks. The second essay found that mutual acquaintance and vehicle affiliations facilitate future co-offending link formation in a real-world criminal network. The third essay found that homophily in programming language preference, and mutual are determinants for forming participation links in an online Open Source social network. The fourth essay focuses on the link prediction in evolving social networks. It proposes a novel infrastructure for describing and utilizing the discovered determinants of link formation process (i.e. semantics of social networks) in link prediction to support expert recommendation application in an Open Source developer community. It is found that the integrated mechanism outperforms either user-based or Top-N most recognized mechanism.

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.