Web Mining and Social Networking: Techniques and ... - tud.ttu.ee

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VIIIPrefacefollowing characteristics: the data on the Web is huge in amount, distributed, heterogeneous,unstructured, and dynamic. To deal withe the heterogeneity and complexitycharacteristics of Web data, Web community has emerged as a new efficient Webdata management means to model Web objects. Unlike the conventional databasemanagement, in which data models and schemas are well defined, Web community,which is a set of Web-based objects (documents and users) has its own logical structures.Web communities could be modeled as Web page groups, Web user clustersand co-clusters of Web pages and users. Web community construction is realizedvia various approaches on Web textual, linkage, usage, semantic or ontology-basedanalysis. Recently the research of Social Network Analysis in the Web has become anewly active topic due to the prevalence of Web 2.0 technologies, which results in aninter-disciplinary research area of Social Networking. Social networking refers to theprocess of capturing the social and societal characteristics of networked structures orcommunities over the Web. Social networking research involves in the combinationof a variety of research paradigms, such as Web mining, Web communities, socialnetwork analysis and behavioral and cognitive modeling and so on.This book will systematically address the theories, techniques and applicationsthat are involved in Web Mining, Social Networking, Web Personalization and Recommendationand Web Community Analysis topics. It covers the algorithmic andtechnical topics on Web mining, namely, Web Content Mining, Web linkage Miningand Web Usage Mining. As an application of Web mining, in particular, Web Personalizationand Recommendation is intensively presented. Another main part discussedin this book is Web Community Analysis and Social Networking. All technical contentsare structured and discussed together around the focuses of Web mining andSocial Networking at three levels of theoretical background, algorithmic descriptionand practical applications.This book will start with a brief introduction on Information Retrieval and WebData Management. For easily and better understanding the algorithms, techniquesand prototypes that are described in the following sections, some mathematical notationsand theoretical backgrounds are presented on the basis of Information Retrieval(IR), Nature Language Processing, Data Mining (DM), Knowledge Discovery (KD)and Machine Learning (ML) theories. Then the principles, and developed algorithmsand systems on the research of Web Mining, Web Recommendation and Personalization,and Web Community and Social Network Analysis are presented in details inseven chapters. Moreover, this book will also focus on the applications of Web mining,such as how to utilize the knowledge mined from the aforementioned processfor advanced Web applications. Particularly, the issues on how to incorporate Webmining into Web personalization and recommendation systems will be substantiallyaddressed accordingly. Upon the informative Web knowledge discovered via Webmining, we then address Web community mining and social networking analysis tofind the structural, organizational and temporal developments of Web communitiesas well as to reveal the societal sense of individuals or communities and its evolutionover the Web by combining social network analysis. Finally, this book willsummarize the main work mentioned regarding the techniques and applications of
PrefaceIXWeb mining, Web community and social network analysis, and outline the futuredirections and open questions in these areas.This book is expected to benefit both research academia and industry communities,who are interested in the techniques and applications of Web search, Web datamanagement, Web mining and Web recommendation as well as Web community andsocial network analysis, for either in-depth academic research and industrial developmentin related areas.Aalborg, Melbourne, WuhanJuly 2010Guandong XuYanchun ZhangLin Li
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194 9 Conclusionsresearches have de
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196 References14. J. Ayres, J. Gehr
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208 References223. C. Shahabi, A. M
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