Download Personalization Techniques And Recommender Systems (Series in Machine Perception and Artificial Inte PDF

Personalization Techniques And Recommender Systems (Series in Machine Perception and Artificial Inte
Name: Personalization Techniques And Recommender Systems (Series in Machine Perception and Artificial Inte
Author: gulden uchyigit
Pages: 334
Year: 2008
Language: English
File Size: 6.91 MB
Downloads: 0
Page 3

SERIES IN MACHINE PERCEPTION AND ARTIFICIAL INTELLIGENCE* Editors:H. Bunke (Univ. Bern, Switzerland) P. S. P. Wang (Northeastern Univ., USA) Vol. 55:Web Document Analysis: Challenges and Opportunities (Eds. A. Antonacopoulos and J. Hu) Vol. 56:Artificial Intelligence Methods in Software Testing (Eds. M. Last, A. Kandel and H. Bunke) Vol. 57:Data Mining in Time Series Databases y (Eds. M. Last, A. Kandel and H. Bunke) Vol. 58:Computational Web Intelligence: Intelligent Technology for Web Applications (Eds. Y. Zhang, A. Kandel, T. Y. Lin and Y. Yao) Vol. 59:Fuzzy Neural Network Theory and Application (P. Liu and H. Li) Vol. 60:Robust Range Image Registration Using Genetic Algorithms and the Surface Interpenetration Measure (L. Silva, O. R. P. Bellon and K. L. Boyer) Vol. 61:Decomposition Methodology for Knowledge Discovery and Data Mining: Theory and Applications (O. Maimon and L. Rokach) Vol. 62:Graph Theoretic Techniques for Web Content Mining (A. Schenker, H. Bunke, M. Last and A. Kandel) Vol. 63: Computational Intelligence in Software Quality Assurance (S. Dick and A. Kandel) Vol. 64:The Dissimilarity Representation for Pattern Recognition: Foundations and Applications (El bieta P"kalska and Robert P. W. Duin) Vol. 65:Fighting Terror in Cyberspace (Eds. M. Last and A. Kandel) Vol. 66:Formal Models, Languages and Applications (Eds. K. G. Subramanian, K. Rangarajan and M. Mukund) Vol. 67:Image Pattern Recognition: Synthesis and Analysis in Biometrics (Eds. S. N. Yanushkevich, P. S. P. Wang, M. L. Gavrilova and S. N. Srihari) Vol. 68Bridging the Gap Between Graph Edit Distance and Kernel Machines (M. Neuhaus and H. Bunke) Vol. 69Data Mining with Decision Trees: Theory and Applications (L. Rokach and O. Maimon) Vol. 70Personalization Techniques and Recommender Systems (Eds. G. Uchyigit and M. Ma)*For the complete list of titles in this series, please write to the Publisher.Steven Personalization Techniques.pmd1/21/2008, 2:34 PM2


Page 5

British Library Cataloguing in Publication Data A catalogue record for this book is available from the British Library. For photocopying of material in this volume, please pay a copying fee through the Copyright Clearance Center, Inc., 222 Rosewood Drive, Danvers, MA 01923, USA. In this case permission to photocopy is not required from the publisher. ISBN 13978 981 279 701 8 ISBN 10981 279 701 7All rights reserved. This book, or parts thereof, may not be reproduced in any form or by any means, electronic or mechanical, including photocopying, recording or any information storage and retrieval system now known or to be invented, without written permission from the Publisher.Copyright 2008 by World Scientific Publishing Co. Pte. Ltd.Published by World Scientific Publishing Co. Pte. Ltd. 5 Toh Tuck Link, Singapore 596224 USA office: 27 Warren Street, Suite 401 402, Hackensack, NJ 07601 UK office: 57 Shelton Street, Covent Garden, London WC2H 9HE Printed in Singapore. PERSONALIZATION TECHNIQUES AND RECOMMENDER SYSTEMS Series in Machine Perception and Artificial Intelligence Vol. 70Steven Personalization Techniques.pmd1/21/2008, 2:34 PM1


Page 6

February20,20082:12WorldScienti cReviewVolume 9inx6inws rv9x6 Preface ThephenomenalgrowthoftheInternethasresultedintheavailabilityof hugeamountsofonlineinformation,asituationthatisoverwhelmingto theend user.Toovercomethisproblempersonalizationtechnologieshave beenextensivelyemployedacrossseveraldomainstoprovideassistancein ltering,sorting,classifyingandsharingofonlineinformation. Theobjectiveofthisbookistofostertheinterdisciplinarydiscussions andresearchinthediversityofpersonalizationandrecommendationtech niques.Thesetechniquesdependonvarioussourcessuchasdomainknowl edge,usermodelinganduserdemographics.These eldsofresearchare nowbeingcoveredbyseveralcrossdisciplinarysocieties.Itisalsothegoal ofthisbooktofosterthediscussionsbetweenresearchersinpatternrecog nitioncommunityandthoseinothersocieties,andaddresspersonalization techniquesatabroaderlevel. The rstInternationalWorkshoponWebPersonalization,Recom menderSystemsandIntelligentUserInterfaces(WPRSIUI'05)wasorga nizedtoaddressissuesrelatedtouserinterfaces,personalizationtechniques andrecommendersystems.ItwasheldinReading,UKinOctober2005. Theprogramcommitteeconsistedofagroupofwell knownresearchersand practitionersinthearea.Twentypaperswerepresentedattheworkshop, thetopicsrangingfromusermodeling,andmachinelearning,tointelli gentuserinterfacesandrecommendersystems.Tosolicitpapersforthis book,authorsofthebestpapersfromtheworkshopwereinvitedtoresub mittheirextendedversionsforreviewalongwithotherpaperssubmitted throughtheopencall.Afteraprestigiousselectionprocessinvolvingtwo roundsofcommitteereviewingfollowedbyeditors' nalreview,weare delightedtopresentthefollowingtwelve(12)papers. The rstpaperPersonalization PrivacyTradeo sinAdaptiveInfor mationAccess"isaninvitedcontributionbyProf.BarrySmyth.This paperpresentsthechallengesofadaptingdi erentdevicessuchasmobile phonestoaccessonlineinformation. v


Page 7

February20,20082:12WorldScienti cReviewVolume 9inx6inws rv9x6 viPreface Thenextthreepapersdiscussissuesrelatedtousermodelingtechniques. InADeepEvaluationofTwoCognitiveUserModelsforPersonalized Search",FabioGasparettiandAlessandroMicarellipresentanewtech niqueforusermodellingwhichimplicitlymodelstheuser'spreferences.In UnobtrusiveUserModelingforAdaptiveHypermedia",HilaryHolz,Katja HofmannandCatherineReedpresentausermodelingtechniquewhichim plicitlymodelstheuser'spreferencesinaneducationaladaptivehypermedia system.InUserModellingSharingforAdaptivee LearningandIntelligent Help",KaterinaKabassi,MariaVirvouandGeorgeTsihrintzispresenta usermodelingserverwithreasoningcapabilitybasedonmulticriteriadeci sionmakingtheory. Continuingonfromtheusermodelingthemethenextthreepapers discussissuesrelatedtocollaborative ltering.InExperimentalAnal ysisofDesignChoicesinMulti AttributeUtilityCollaborativeFiltering onaSyntheticDataSet",NikosManouselisandConstantinaCostopoulou presenttheexperimentalanalysisofseveraldesignoptionsforthreepro posedmultiattributeutilitycollaborative lteringalgorithms.InE cient CollaborativeFilteringinContent AddressableSpaces",ShlomoBerkovsky, YanivEytaniandLarryManevitzdescribeafastheuristicvariantofacol laborative lteringalgorithmthatdecreasesthecomputationale ortre quiredbythesimilaritycomputationandneighbourhoodformationstages. InIdentifyingandAnalyzingUserModelInformationfromCollaborative FilteringDatasets",JosephineGri th,ColmO'RiordanandHumphrey Sorensenpresentatechniqueofextractingfeaturesfromthecollaborative lteringdatasetstobeusedinmodelinggroupsofusers. Finallythelast vepapersdiscussissuesrelatedtocontent basedrec ommendersystems,hybirdsystemsandmachinelearningmethods.In PersonalizationandSemanticReasoninginAdvancedRecommenderSys tems",YolandaBlancoFernandez,JosePazosArias,AlbertoGilSolla, ManuelRamosCabrerandMartinLopezNorespresentahybrid based recommendersystemframeworkwhichusessemanticinformationforuser modeling.InContentClassi cationandRecommendationTechniquesfor ViewingElectronicProgrammingGuideonaPortableDevice",JingboZhu, MatthewMa,JinghongGuoandZhenxingWangpresentacontent based recommendersystemwhichpresentsapersonalizedbrowsingandrecom mendationsofTVprograms.InUserAcceptanceofKnowledge basedRec ommenders",AlexanderFelfering,EricTeppanandBartoszGulapresenta knowledgebasedrecommendersystemfore commerce.InRestrictedRan domWalksforLibraryRecommendations",MarkusFrankeandAndreas


Page 8

February20,20082:12WorldScienti cReviewVolume 9inx6inws rv9x6 Prefacevii Geyer Schulzpresentanimplicitrecommendersystemwhichusesrestricted randomwalksforalibraryapplicationsystem.InAnExperimentalStudy ofFeatureSelectionMethodsforTextClassi cation",GuldenUchyigitand KeithClarkpresentacomparativestudyoffeatureselectionmethod.The abovetwelvepapersrepresentmanyinterestingresearche ortsandcover severalmaincategoriesofpersonalizationandrecommendation.Thisbook isdedicatedtobringingtogetherrecentadvancementsofpersonalization techniquesforrecommendersystemsanduserinterfaces.Itisalsoofpar ticularinteresttoresearchersinindustryintendingtodeployadvanced techniquesintheirsystems. Acknowledgment Theeditorswouldliketoacknowledgethecontributionandsupportfromall authorsinthisbookandmanyofinvaluablecommentsfromourreviewers includingtheprogramcommitteeofthe rstInternationalWorkshopon WebPersonalization,RecommenderSystemsandIntelligentUserInterfaces (WPRSIUI'05).Theyare:LilianaArdissono,MarkoBalabanovic,Chumki Basu,RobinBurke,JoaquinDelagado,JinhongK.Guo,XiaoyiJiang,Mark Levene,SofusMacskassy,DunjaMladenic,IanSoboro ,DavidTaniar, PatrickWangandJingboZhu. Finally,wewouldliketoexpressourgratitudetoProf.X.Jiangand Prof.P.S.P.Wang,theeditors in chiefofInternationalJournalofPattern RecognitionandArti cialIntelligence(IJPRAI). G.UchyigitandM.Ma


Tags: Download Personalization Techniques And Recommender Systems (Series in Machine Perception and Artificial Inte PDF, Personalization Techniques And Recommender Systems (Series in Machine Perception and Artificial Inte free pdf download, Personalization Techniques And Recommender Systems (Series in Machine Perception and Artificial Inte Pdf online download, Personalization Techniques And Recommender Systems (Series in Machine Perception and Artificial Inte By gulden uchyigit download, Personalization Techniques And Recommender Systems (Series in Machine Perception and Artificial Inte.pdf, Personalization Techniques And Recommender Systems (Series in Machine Perception and Artificial Inte read online.
About | Contact | DMCA | Terms | Privacy | Mobile Specifications
Copyright 2021 FilePdf