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168 7 Extracting and Analyzing Web Social NetworksFig. 7.21. Popularity evolution of a WOM sourceFig. 7.22. Comparison of commodity brands
8Web Mining and Recommendation SystemsIn last chapter, we have selectively addressed several interesting topics on Web community andsocial network detecting, forming and analysis, especially temporal and evolutionary analysis.We have discussed the motivations of such kinds of techniques, the algorithmic issues, and theexperimental studies as well as the insightful findings and results. In this chapter, we will shiftto another important application of Web data mining: Web recommendation.8.1 User-based and Item-based Collaborative FilteringRecommender SystemsNowadays the Internet has been well known as a big data repository consisting of a variety ofdata types as well as a large amount of unseen informative knowledge, which can be discoveredvia a wide range of data mining or machine learning paradigms. Although the progress ofthe Web-based data management research results in developments of many useful Web applicationsor services, like Web search engines, users are still facing the problems of informationoverload and drowning due to the significant and rapid growth in amount of information andthe number of users. In particular, Web users usually suffer from the difficulties of findingdesirable and accurate information on the Web due to two problems of low precision and lowrecall caused by above reasons.Web (data) mining could be partly used to solve the problems mentioned above directlyor indirectly. In principle, Web mining is the means of utilizing data mining methods to induceand extract useful information from Web data information. By utilizing the informativeknowledge learned from Web mining, we can substantially improve the Web search performanceand user satisfaction. Additionally, another most promising technique for this is Webrecommendation.Web recommendation or personalization could be viewed as a process that recommendscustomized Web presentation or predicts tailored Web content to Web users according to theirspecific tastes or preferences. To-date, there are two kinds of approaches commonly usedin recommender systems, namely content-based filtering and collaborative filtering systems[81, 117]. Content-based filtering systems such as WebWatcher [129] and client-side agentLetizia [163] usually generate recommendation based on the pre-constructed user profiles bymeasuring the similarity of Web content to these profiles, while collaborative filtering systemsmake recommendation by referring other users’ preference that is closely similar to currentG. Xu et al., Web Mining and Social Networking,DOI 10.1007/978-1-4419-7735-9_8, © Springer Science+Business Media, LLC 2011
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Web Mining and Social Networking
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Guandong Xu • Yanchun Zhang • L
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VIIIPrefacefollowing characteristic
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Acknowledgements: We would like to
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XIVContents3.1.2 Basic Algorithms f
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XVIContentsPart III Social Networki
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Part IFoundation
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4 1 Introduction(3). Learning usefu
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6 1 Introductioncalled computationa
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8 1 Introduction• The data on the
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10 1 Introductionin a broad range t
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2Theoretical BackgroundsAs discusse
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2.2 Textual, Linkage and Usage Expr
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2.4 Eigenvector, Principal Eigenvec
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2.5 Singular Value Decomposition (S
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2.6 Tensor Expression and Decomposi
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2.7 Information Retrieval Performan
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2.8 Basic Concepts in Social Networ
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2.8 Basic Concepts in Social Networ
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30 3 Algorithms and TechniquesTable
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64 3 Algorithms and Techniques3.8.2
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68 3 Algorithms and Techniquesthat
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4Web Content MiningIn recent years
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score(q,d)=4.2 Web Search 73V(q) ·
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4.2 Web Search 75algorithm. The Web
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4.3 Feature Enrichment of Short Tex
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4.4 Latent Semantic Indexing 794.4
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Notation4.5 Automatic Topic Extract
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4.5 Automatic Topic Extraction from
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4.6 Opinion Search and Opinion Spam
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4.6 Opinion Search and Opinion Spam
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90 5 Web Linkage Mining5.2 Co-citat
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94 5 Web Linkage Mininga =(a(1),·
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96 5 Web Linkage Mining5.4.1 Bipart
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98 5 Web Linkage MiningNext, consid
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100 5 Web Linkage Mining(5) Creatin
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102 5 Web Linkage Miningpower-law d
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104 5 Web Linkage MiningFig. 5.10.
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106 5 Web Linkage Miningbetween use
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6Web Usage MiningIn previous chapte
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6.1 Modeling Web User Interests usi
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Page 226 and 227: 208 References223. C. Shahabi, A. M
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