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

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122 6 Web Usage MiningExamples of Latent Semantic FactorsIn this section, we present some results regarding Web usage pattern and latent semantic factorobtained by conducting experiments on two selected Web log datasets. The first dataset isnamed KDDCUP 1 . In this data set, the entries in session-page matrix are determined by thenumber of Web page hits since the number of a user coming back to a specific page is agood measure to reflect the user interest on the page. The second data set is downloaded frommsnbc.com 2 , which describes the page visits by users who visited msnbc.com on September28, 1999. This data set is named as “msnbc” dataset.The experimental results on these two datasets are tabulated in Table 6.1 and 6.2. Table 6.1first lists 13 extracted latent factors and their corresponding characteristic descriptions fromKDDCUP dataset. And Table 6.2 depicts 3 factor examples selected from whole factor spacein terms of associated page information including page number, probability and description.From this table, it is seen that factor #3 indicates the concerns about vendor service messagesuch as customer service, contact number, payment methods as well as delivery support. Thefactor #7 describes the specific progress which may include customer login, product order,express checkout and financial information input such steps occurred in Internet shoppingscenario, whereas factors #13 actually captures another focus exhibited by Web content, whichreveals the fact that some Web users may pay more attentions to the information regardingdepartment itself..Table 6.1. Latent factors and their characteristic descriptions from KDDCUPFactor # Characteristic title1 Department search results2 ProductDetailLegwear3 Vendor service4 Freegift5 ProductDetailLegcare6 Shopping cart7 Online shopping8 Lifestyle assortment9 Assortment210 Boutique11 Departmet replenishment12 Department article13 Home pageAs for ”msnbc” dataset, it is hard to extract the exact latent factor space as the pageinformation provided is described at a coarser granularity level, i.e. URL category level. Hencewe only list two examples of discovered latent factors to illustrate the general usage knowledge1 www.ecn.purdue.edu/kddcup2 http://kdd.ics.uci.edu/databases/
6.2 Web Usage Mining using Probabilistic Latent Semantic Analysis 123Table 6.2. Factor examples and their associated page information from KDDCUPFactor # Page # P(p j |z k ) Page description10 0.865 main/vendor\.jhtml36 0.035 main/cust serv˙jhtml37 0.021 articles/dpt contact\.jhtml3 39 0.020 articles/dpt shipping\.jhtml38 0.016 articles/dpt payment\.jhtml41 0.016 articles/dpt faqs\.jhtml40 0.013 articles/dpt returns\.jhtml27 0.249 main/login2\.jhtml44 0.18 checkout/expresCheckout.jhmt32 0.141 main/registration\.jhtml7 65 0.135 main/welcome\.jhtml45 0.135 checkout/confirm order\.jhtml42 0.045 account/your account\.jhtml60 0.040 checkout/thankyou\.jhtml12 0.232 articles/dpt about\.jhtml22 0.127 articles/new shipping\.jhtml13 0.087 articles/dpt about mgmtteam13 14 0.058 articles\dpt about boardofdirectors20 0.058 articles/dpt affiliate\.jhtml16 0.053 articles/dpt about careers19 0.052 articles/dpt refer\.jhtml23 0.051 articles/new returns\.jhtmlhidden in the usage data (shown in Table 6.3). The two extracted factors indicate that factor#1 is associated with all kinds of local information that come from miscellaneous informationchannel such as bbs, while factor #2 reflects the interests or opinions which are often linkedwith health, sport as well as technical development in physical exercise.Table 6.3. Factor examples from msnbc data setFactor # Category # P(p j |z k ) URL category4 0.316 local7 0.313 misc1 6 0.295 on-air15 0.047 summary16 0.029 bbs10 0.299 health2 5 0.262 opinion13 0.237 msn-sport3 0.203 tech
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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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190 9 Conclusionsries commonly used
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192 9 Conclusionsas computer scienc
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194 9 Conclusionsresearches have de
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196 References14. J. Ayres, J. Gehr
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198 References49. D. Chakrabarti, R
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200 References82. C. Dwork, R. Kuma
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202 References119. J. Hou and Y. Zh
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204 References151. A. N. Langville
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206 References186. J. K. Mui and K.
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208 References223. C. Shahabi, A. M
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210 References260. G.-R. Xue, D. Sh
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