Personalization Techniques And Recommender Systems (Series in Machine Perception and Artificial Intelligence ???) (Series in Machine Perception and Artificial ... Perception and Artifical Intelligence)

334 Pages·2008·6.91 MB·English

by  Gulden Uchyigit

PERSONALIZATION
TECHNIQUES AND
RECOMMENDER SYSTEMS
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
(Elóbieta 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. 68 Bridging the Gap Between Graph Edit Distance and Kernel Machines
(M. Neuhaus and H. Bunke)
Vol. 69 Data Mining with Decision Trees: Theory and Applications
(L. Rokach and O. Maimon)
Vol. 70 Personalization 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.pmd 2 1/21/2008, 2:34 PM
Series in Machine Perception and Artificial Intelligence - Vol. 70
PERSONALIZATION
TECHNIQUES AND
RECOMMENDER SYSTEMS
Editors
Gulden Uchyigit
Imperial College, UK
Matthew Y. Ma
Scientific Works, USA
r p
World Scientific
- - - -
NEW JERSEY LONDON SINGAPORE BElJlNG SHANGHAI * HONG KONG * TAIPEI CHENNAI
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
British Library Cataloguing-in-Publication Data
A catalogue record for this book is available from the British Library.
PERSONALIZATION TECHNIQUES AND RECOMMENDER SYSTEMS
Series in Machine Perception and Artificial Intelligence — Vol. 70
Copyright © 2008 by World Scientific Publishing Co. Pte. Ltd.
All 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.
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-13 978-981-279-701-8
ISBN-10 981-279-701-7
Printed in Singapore.
Steven - Personalization Techniques.pmd 1 1/21/2008, 2:34 PM
February20,2008 2:12 WorldScienti(cid:12)cReviewVolume-9inx6in ws-rv9x6
Preface
The phenomenal growth of the Internet has resulted in the availability of
huge amounts of online information, a situation that is overwhelming to
the end-user. To overcome this problem personalization technologies have
been extensively employed across several domains to provide assistance in
(cid:12)ltering, sorting, classifying and sharing of online information.
The objective of this book is to foster the interdisciplinary discussions
and research in the diversity of personalization and recommendation tech-
niques. Thesetechniquesdependonvarioussourcessuchasdomainknowl-
edge, user modeling and user demographics. These (cid:12)elds of research are
nowbeing coveredbyseveralcrossdisciplinary societies. It is alsothe goal
of this book to foster the discussions between researchersin pattern recog-
nition communityandthose inother societies, andaddresspersonalization
techniques at a broader level.
The (cid:12)rst International Workshop on Web Personalization, Recom-
mender Systems and Intelligent User Interfaces (WPRSIUI’05) was orga-
nizedtoaddressissuesrelatedtouserinterfaces,personalizationtechniques
and recommender systems. It was held in Reading, UK in October 2005.
Theprogramcommitteeconsistedofagroupofwell-knownresearchersand
practitioners in the area. Twenty papers were presented at the workshop,
the topics ranging from user modeling, and machine learning, to intelli-
gent user interfaces and recommender systems. To solicit papers for this
book, authorsof the best papers from the workshopwere invited to resub-
mit their extended versions for review along with other papers submitted
through the open call. After a prestigious selection process involving two
rounds of committee reviewing followed by editors’ (cid:12)nal review, we are
delighted to present the following twelve (12) papers.
The (cid:12)rst paper \Personalization-Privacy Tradeo(cid:11)s in Adaptive Infor-
mation Access" is an invited contribution by Prof. Barry Smyth. This
paper presents the challenges of adapting di(cid:11)erent devices such as mobile
phones to access online information.
v
February20,2008 2:12 WorldScienti(cid:12)cReviewVolume-9inx6in ws-rv9x6
vi Preface
Thenextthreepapersdiscussissuesrelatedtousermodelingtechniques.
In \A Deep Evaluation of Two Cognitive User Models for Personalized
Search ", Fabio Gasparetti and Alessandro Micarelli present a new tech-
nique for user modelling which implicitly models the user’s preferences. In
\UnobtrusiveUserModelingforAdaptiveHypermedia",HilaryHolz,Katja
HofmannandCatherineReedpresentausermodelingtechniquewhichim-
plicitlymodelstheuser’spreferencesinaneducationaladaptivehypermedia
system. In\UserModellingSharingforAdaptivee-LearningandIntelligent
Help", Katerina Kabassi, Maria Virvou and George Tsihrintzis present a
user modeling serverwith reasoningcapability based on multicriteriadeci-
sion making theory.
Continuing on from the user modeling theme the next three papers
discuss issues related to collaborative (cid:12)ltering. In \Experimental Anal-
ysis of Design Choices in Multi-Attribute Utility Collaborative Filtering
on a Synthetic Data Set", Nikos Manouselisand ConstantinaCostopoulou
present the experimental analysis of several design options for three pro-
posedmultiattribute utility collaborative(cid:12)lteringalgorithms. In \E(cid:14)cient
CollaborativeFilteringinContent-AddressableSpaces",ShlomoBerkovsky,
YanivEytaniandLarryManevitzdescribeafast heuristicvariantof a col-
laborative (cid:12)ltering algorithm that decreases the computational e(cid:11)ort re-
quiredbythe similaritycomputationandneighbourhoodformationstages.
In \Identifying and Analyzing User Model Information from Collaborative
Filtering Datasets ", Josephine Gri(cid:14)th, Colm O’Riordan and Humphrey
Sorensen present a technique of extracting features from the collaborative
(cid:12)ltering datasets to be used in modeling groups of users.
Finally the last (cid:12)ve papers discuss issues related to content-based rec-
ommender systems, hybird systems and machine learning methods. In
\Personalizationand Semantic Reasoningin Advanced Recommender Sys-
tems", Yolanda Blanco Fernandez, Jose Pazos Arias, Alberto Gil Solla,
Manuel Ramos Cabrer and Martin Lopez Nores present a hybrid-based
recommender system framework which uses semantic information for user
modeling. In \Content Classi(cid:12)cation and Recommendation Techniques for
ViewingElectronicProgrammingGuideonaPortableDevice",JingboZhu,
Matthew Ma, Jinghong Guo and Zhenxing Wang present a content-based
recommender system which presents a personalized browsing and recom-
mendationsofTVprograms. In\UserAcceptanceofKnowledge-basedRec-
ommenders",AlexanderFelfering,EricTeppanandBartoszGulapresenta
knowledgebasedrecommendersystemfore-commerce. In\RestrictedRan-
dom Walks for Library Recommendations", Markus Franke and Andreas
February20,2008 2:12 WorldScienti(cid:12)cReviewVolume-9inx6in ws-rv9x6
Preface vii
Geyer-Schulzpresentanimplicitrecommendersystemwhichusesrestricted
randomwalksforalibraryapplicationsystem. In\AnExperimentalStudy
ofFeatureSelectionMethodsforTextClassi(cid:12)cation",GuldenUchyigitand
Keith Clark present a comparativestudy of feature selection method. The
above twelve papers represent many interesting research e(cid:11)orts and cover
severalmaincategoriesof personalizationandrecommendation. Thisbook
is dedicated to bringing together recent advancements of personalization
techniques for recommender systems and user interfaces. It is also of par-
ticular interest to researchers in industry intending to deploy advanced
techniques in their systems.
Acknowledgment
Theeditorswouldliketoacknowledgethecontributionandsupportfromall
authors in this book and many of invaluable comments from our reviewers
including the program committee of the (cid:12)rst International Workshop on
WebPersonalization,RecommenderSystemsandIntelligentUserInterfaces
(WPRSIUI’05). Theyare: LilianaArdissono,MarkoBalabanovic,Chumki
Basu,RobinBurke,JoaquinDelagado,JinhongK.Guo,XiaoyiJiang,Mark
Levene, Sofus Macskassy, Dunja Mladenic, Ian Soboro(cid:11), David Taniar,
Patrick Wang and Jingbo Zhu.
Finally, we would like to express our gratitude to Prof. X. Jiang and
Prof. P.S.P. Wang, the editors-in-chief of International Journal of Pattern
Recognition and Arti(cid:12)cial Intelligence (IJPRAI).
G. Uchyigit and M. Ma
February20,2008 2:12 WorldScienti(cid:12)cReviewVolume-9inx6in ws-rv9x6
TThhiiss ppaaggee iinntteennttiioonnaallllyy lleefftt bbllaannkk
February20,2008 2:12 WorldScienti(cid:12)cReviewVolume-9inx6in ws-rv9x6
Contents
Preface v
User Modeling and Pro(cid:12)ling 1
1. Personalization-Privacy Tradeo(cid:11)s in Adaptive Informa-
tion Access 3
B. Smyth
2. A Deep Evaluation of Two Cognitive User Models for
Personalized Search 33
F. Gasparetti and A. Micarelli
3. Unobtrusive User Modeling For Adaptive Hypermedia 61
H. J. Holz, K. Hofmann and C. Reed
4. User Modelling Sharing for Adaptive e-Learning and In-
telligent Help 85
K. Kabassi, M. Virvou and G. A. Tsihrintzis
Collaborative Filtering 109
5. ExperimentalAnalysisof Multiattribute Utility Collabo-
rative Filtering on a Synthetic Data Set 111
N. Manouselis and C. Costopoulou
ix