SOCIALQ&A: A NOVEL APPROACH TO NOTIFIYING THE CORRECT USERS IN QUESTION AND ANSWERING SYSTEMS

Question and Answering (Q&A) systems are currently in use by a large number of Internet users. Q&A systems play a vital role in our daily life as an important platform for information and knowledge sharing. Hence, much research has been devoted to improving the performance of Q&A systems, with a focus on improving the quality of answers provided by users, reducing the wait time for users who ask questions, using a knowledge base to provide answers via text mining, and directing questions to appropriate users. Due to the growing popularity of Q&A systems, the number of questions in the system can become very large; thus, it is unlikely for an answer provider to simply stumble upon a question that he/she can answer properly. The primary objective of this research is to improve the quality of answers and to decrease wait times by forwarding questions to users who exhibit an interest or expertise in the area to which the question belongs. To that end, this research studies how to leverage social networks to enhance the performance of Q&A systems. We have proposed SocialQ&A, a social network based Q&A system that identifies and notifies the users who are most likely to answer a question. SocialQ&A incorporates three major components: User Interest Analyzer, Question Categorizer, and Question- User Mapper. The User Interest Analyzer associates each user with a vector of interest categories. The Question Categorizer algorithm associates a vector of interest categories to each question. Then, based on user interest and user social connectedness, the Question-User Mapper identifies a list of potential answer providers for each question. We have also implemented a real-world prototype for SocialQ&A and analyzed the data from questions/answers obtained from the prototype. Results suggest that social networks can be leveraged to improve the quality of answers and reduce the wait time for answers. Thus, this research provides a promising direction to improve the performance of Q&A systems

Incorporating Participant Reputation in Community-driven Question Answering Systems

Community-driven Question Answering services are gaining increasing attention with tens of millions of users and hundreds of millions of posts in recent years. Due to its size, there is a need for users to be able to search these large question answer archives and retrieve high quality content. Research work shows that user reputation modeling makes a contribution when incorporated with relevance models. However, the effectiveness of different link analysis approaches and how to embed topical information—as a user may have different expertise in various areas—are still open questions. In this work, we address these two research questions by first reviewing different link analysis schemes—especially discussing the use of PageRank-based methods since they are less commonly utilized in user reputation modeling. We also introduce Topical Page-Rank analysis for modeling user reputation on different topics. Comparative experimental results on data from Yahoo! Answers show that PageRank-based approaches are more effective than HITS-like schemes and other heuristics, and that topical link analysis can improve performance

Mining Web Dynamics for Search

Billions of web users collectively contribute to a dynamic web that preserves how information sources and descriptions change over time. This dynamic process sheds light on the quality of web content, and even indicates the temporal properties of information needs expressed via queries. However, existing commercial search engines typically utilize one crawl of web content (the latest) without considering the complementary information concealed in web dynamics. As a result, the generated rankings may be biased due to the efficiency of knowledge on page or hyperlink evolution, and the time-sensitive facet within search quality, e.g., freshness, has to be neglected. While previous research efforts have been focused on exploring the temporal dimension in retrieval process, few of them showed consistent improvements on large-scale real-world archival web corpus with a broad time span.We investigate how to utilize the changes of web pages and hyperlinks to improve search quality, in terms of freshness and relevance of search results. Three applications that I have focused on are: (1) document representation, in which the anchortext (short descriptive text associated with hyperlinks) importance is estimated by considering its historical status; (2) web authority estimation, in which web freshness is quantified and utilized for controlling the authority propagation; and (3) learning to rank, in which freshness and relevance are optimized simultaneously in an adaptive way depending on query type. The contributions of this thesis are: (1) incorporate web dynamics information into critical components within search infrastructure in a principled way; and (2) empirically verify the proposed methods by conducting experiments based on (or depending on) a large-scale real-world archival web corpus, and demonstrated their superiority over existing state-of-the-art