LEADER 05693nam 2200709Ia 450 001 9910453207203321 005 20200520144314.0 010 $a1-281-94811-X 010 $a9786611948115 010 $a981-279-702-5 035 $a(CKB)1000000000538207 035 $a(EBL)1679488 035 $a(OCoLC)879023655 035 $a(SSID)ssj0000220182 035 $a(PQKBManifestationID)11910753 035 $a(PQKBTitleCode)TC0000220182 035 $a(PQKBWorkID)10143315 035 $a(PQKB)10958399 035 $a(MiAaPQ)EBC1679488 035 $a(WSP)00001966 035 $a(Au-PeEL)EBL1679488 035 $a(CaPaEBR)ebr10255447 035 $a(CaONFJC)MIL194811 035 $a(EXLCZ)991000000000538207 100 $a20080322d2008 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 00$aPersonalization techniques and recommender systems$b[electronic resource] /$feditors, Gulden Uchyigit, Matthew Y. Ma 210 $aHackensack, NJ $cWorld Scientific$dc2008 215 $a1 online resource (334 p.) 225 1 $aSeries in machine perception and artificial intelligence ;$vv. 70 300 $aDescription based upon print version of record. 311 $a981-279-701-7 320 $aIncludes bibliographical references and index. 327 $aContents; Preface; User Modeling and Profiling; 1. Personalization-Privacy Tradeo s in Adaptive Information Access B. Smyth; 1.1. Introduction; 1.2. Case-Study 1 - Personalized Mobile Portals; 1.2.1. The challenges of mobile information access; 1.2.1.1. Mobile internet devices; 1.2.1.2. Browsing versus search on the mobile internet; 1.2.2. The click-distance problem; 1.2.3. Personalized navigation; 1.2.3.1. Pro ling the user; 1.2.3.2. Personalizing the portal; 1.2.4. Evaluation; 1.2.4.1. Click-distance reduction; 1.2.4.2. Navigation time versus content time 327 $a1.3. Case-Study 2: Personalized Web Search1.3.1. The challenges of web search; 1.3.2. Exploiting repetition and regularity in community- based web search; 1.3.3. A case-based approach to personalizing web search; 1.3.4. Evaluation; 1.3.4.1. Successful sessions; 1.3.4.2. Selection positions; 1.4. Personalization-Privacy: Striking a Balance; 1.5. Conclusions; Acknowledgments; References; BIOGRAPHY; 2. A Deep Evaluation of Two Cognitive User Models for Personalized Search F. Gasparetti and A. Micarelli; 2.1. Introduction; 2.2. Related Work; 2.3. SAM-based User Modeling Approach 327 $a2.3.1. SAM: search of associative memory2.3.2. The user modeling approach; 2.3.2.1. LTS and STS; 2.3.2.2. Sampling and Recovery; 2.3.2.3. Learning; 2.3.2.4. Interaction with Information Sources; 2.3.3. HAL-based User Modeling Approach; 2.4. Evaluation; 2.4.1. Evaluating User Models in Browsing Activities; 2.4.2. Corpus-based evaluation; 2.4.3. Precision vs. Number of Topics; 2.4.4. Precision vs. Extracted Cues; 2.4.5. Precision vs. Size of STS; 2.4.6. Precision vs. Number of Recovery Attempts; 2.5. Conclusions; References; BIOGRAPHIES 327 $a3. Unobtrusive User Modeling For Adaptive Hypermedia H. J. Holz, K. Hofmann and C. Reed3.1. Introduction; 3.1.1. User modeling in adaptive hypermedia; 3.1.2. Motivation: informal education and the user modeling effect; 3.1.3. Our solution: unobtrusive user modeling; 3.2. Approach; 3.2.1. Classi er-independent feature selection; 3.2.2. Inference design; 3.3. Field Study; 3.3.1. ACUT; 3.3.2. Measurements; 3.3.3. Feature design; 3.3.4. Data collection; 3.3.5. Self-organizing maps; 3.3.6. Revising the features; 3.4. Discussion; Acknowledgments; References; BIOGRAPHIES 327 $a4. User Modelling Sharing for Adaptive e-Learning and Intelligent Help K. Kabassi, M. Virvou and G. A. Tsihrintzis4.1. Introduction; 4.2. Description of Systems of Di erent Domains Sharing a Common User Model; 4.2.1. System for e-Learning in Atheromatosis; 4.2.2. Systems for Intelligent Help in le manipulation and e-mailing; 4.2.3. Error Diagnosis in three systems of different domains; 4.3. Common attributes for evaluating alternative actions; 4.4. Example of a user interacting with three di erent sys- tems; 4.5. User Modelling based on Web Services; 4.5.1. UM-Server's Architecture 327 $a4.5.2. UM-Server's Operation 330 $a The phenomenal growth of the Internet has resulted in huge amounts of online information, a situation that is overwhelming to the end users. To overcome this problem, personalization technologies have been extensively employed. The book is the first of its kind, representing research efforts in the diversity of personalization and recommendation techniques. These include user modeling, content, collaborative, hybrid and knowledge-based recommender systems. It presents theoretic research in the context of various applications from mobile information access, marketing and sales and web service 410 0$aSeries in machine perception and artificial intelligence ;$vv. 70. 606 $aPersonal communication service systems 606 $aRecommender systems (Information filtering) 606 $aWireless communication systems 608 $aElectronic books. 615 0$aPersonal communication service systems. 615 0$aRecommender systems (Information filtering) 615 0$aWireless communication systems. 676 $a004.0688 701 $aMa$b M. Y$g(Matthew Y.)$0903221 701 $aUchyigit$b G$g(Gulden)$0903222 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910453207203321 996 $aPersonalization techniques and recommender systems$92019056 997 $aUNINA