LEADER 05914nam 22008295 450 001 9910768432503321 005 20251226203856.0 024 7 $a10.1007/11577935 035 $a(CKB)1000000000213359 035 $a(SSID)ssj0000318399 035 $a(PQKBManifestationID)11239926 035 $a(PQKBTitleCode)TC0000318399 035 $a(PQKBWorkID)10308561 035 $a(PQKB)11420090 035 $a(DE-He213)978-3-540-31655-8 035 $a(MiAaPQ)EBC3068331 035 $a(PPN)123098475 035 $a(BIP)13205887 035 $a(EXLCZ)991000000000213359 100 $a20100715d2005 u| 0 101 0 $aeng 135 $aurnn#008mamaa 181 $ctxt 182 $cc 183 $acr 200 10$aIntelligent Techniques for Web Personalization $eIJCAI 2003 Workshop, ITWP 2003, Acapulco, Mexico, August 11, 2003, Revised Selected Papers /$fedited by Bamshad Mobasher, Sarabjot Singh Anand 205 $a1st ed. 2005. 210 1$aBerlin, Heidelberg :$cSpringer Berlin Heidelberg :$cImprint: Springer,$d2005. 215 $a1 online resource (VIII, 328 p.) 225 1 $aLecture Notes in Artificial Intelligence,$x2945-9141 ;$v3169 300 $aBibliographic Level Mode of Issuance: Monograph 311 08$aPrinted edition: 9783540298465 320 $aIncludes bibliographic references and index. 327 $aIntelligent Techniques for Web Personalization -- Intelligent Techniques for Web Personalization -- User Modelling -- Modeling Web Navigation: Methods and Challenges -- The Traits of the Personable -- Addressing Users? Privacy Concerns for Improving Personalization Quality: Towards an Integration of User Studies and Algorithm Evaluation -- Recommender Systems -- Case-Based Recommender Systems: A Unifying View -- Improving the Performance of Recommender Systems That Use Critiquing -- Hybrid Systems for Personalized Recommendations -- Enabling Technologies -- Collaborative Filtering Using Associative Neural Memory -- Scaling Down Candidate Sets Based on the Temporal Feature of Items for Improved Hybrid Recommendations -- Discovering Interesting Navigations on a Web Site Using SAM I -- Personalized Information Access -- Personalisation of Web Search -- The Compass Filter: Search Engine Result Personalization Using Web Communities -- Predicting Web Information Content -- Systems and Applications -- Mobile Portal Personalization: Tools and Techniques -- IKUM: An Integrated Web Personalization Platform Based on Content Structures and User Behavior -- A Semantic-Based User Privacy Protection Framework for Web Services -- Web Personalisation for Users Protection: A Multi-agent Method. 330 $aWeb personalizationcan be de'ned as any set of actions that can tailor the Web experience to a particular user or set of users. The experience can be something as casualas browsinga Web site oras (economically)signi'cantas tradingstock or purchasing a car. The actions can range from simply making the presentation more pleasing to anticipating the needs of a user and providing customized and relevant information. To achieve e'ective personalization, organizations must rely on all available data, including the usage and click-stream data (re'e- ing user behavior), the site content, the site structure, domain knowledge, user demographics and pro'les. In addition, e'cient and intelligent techniques are needed to mine these data for actionable knowledge, and to e'ectively use the discovered knowledge to enhance the users' Web experience. These techniques must address important challenges emanating from the size and the heteroge- ity of the data, and the dynamic nature of user interactions with the Web. E-commerce and Web information systems are rich sources of di'cult pr- lems and challenges for AI researchers. These challenges include the scalability of the personalization solutions, data integration, and successful integration of techniques from machine learning, information retrievaland ?ltering, databases, agent architectures, knowledge representation, data mining, text mining, stat- tics, user modelling and human-computer interaction. Throughout the history of the Web, AI has continued to play an essential role in the development of Web-based information systems, and now it is believed that personalization will prove to be the "killer-app" for AI. 410 0$aLecture Notes in Artificial Intelligence,$x2945-9141 ;$v3169 606 $aArtificial intelligence 606 $aComputer networks 606 $aInformation storage and retrieval systems 606 $aUser interfaces (Computer systems) 606 $aHuman-computer interaction 606 $aComputers and civilization 606 $aBusiness information services 606 $aArtificial Intelligence 606 $aComputer Communication Networks 606 $aInformation Storage and Retrieval 606 $aUser Interfaces and Human Computer Interaction 606 $aComputers and Society 606 $aIT in Business 615 0$aArtificial intelligence. 615 0$aComputer networks. 615 0$aInformation storage and retrieval systems. 615 0$aUser interfaces (Computer systems). 615 0$aHuman-computer interaction. 615 0$aComputers and civilization. 615 0$aBusiness information services. 615 14$aArtificial Intelligence. 615 24$aComputer Communication Networks. 615 24$aInformation Storage and Retrieval. 615 24$aUser Interfaces and Human Computer Interaction. 615 24$aComputers and Society. 615 24$aIT in Business. 676 $a004.678 701 $aMobasher$b Bamshad$0899226 701 $aAnand$b Sarabjot S$01754915 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910768432503321 996 $aIntelligent techniques for Web personalization$94191428 997 $aUNINA