05693nam 2200709Ia 450 991045320720332120200520144314.01-281-94811-X9786611948115981-279-702-5(CKB)1000000000538207(EBL)1679488(OCoLC)879023655(SSID)ssj0000220182(PQKBManifestationID)11910753(PQKBTitleCode)TC0000220182(PQKBWorkID)10143315(PQKB)10958399(MiAaPQ)EBC1679488(WSP)00001966 (Au-PeEL)EBL1679488(CaPaEBR)ebr10255447(CaONFJC)MIL194811(EXLCZ)99100000000053820720080322d2008 uy 0engur|n|---|||||txtccrPersonalization techniques and recommender systems[electronic resource] /editors, Gulden Uchyigit, Matthew Y. MaHackensack, NJ World Scientificc20081 online resource (334 p.)Series in machine perception and artificial intelligence ;v. 70Description based upon print version of record.981-279-701-7 Includes bibliographical references and index.Contents; 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 time1.3. 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 Approach2.3.1. 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; BIOGRAPHIES3. 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; BIOGRAPHIES4. 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 Architecture4.5.2. UM-Server's Operation 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 serviceSeries in machine perception and artificial intelligence ;v. 70.Personal communication service systemsRecommender systems (Information filtering)Wireless communication systemsElectronic books.Personal communication service systems.Recommender systems (Information filtering)Wireless communication systems.004.0688Ma M. Y(Matthew Y.)903221Uchyigit G(Gulden)903222MiAaPQMiAaPQMiAaPQBOOK9910453207203321Personalization techniques and recommender systems2019056UNINA01854oam 2200409 450 991027094110332120210111172432.01-119-41187-41-119-12223-61-119-12222-8(CKB)3710000001363673(DLC) 2017009355(MiAaPQ)EBC4856325(EXLCZ)99371000000136367320170227d2017 uy 0engur|||||||||||rdacontentrdamediardacarrierFundamentals of oral and maxillofacial radiology /J. Sean HubarHoboken, NJ :Wiley,2017.1 online resource1-119-12221-X Includes bibliographical references (pages 238-249) and index.History -- Generation of X rays -- Exposure controls -- Radiation dosimetry -- Radiation biology -- Radiation protection -- Patient selection criteria -- Analog film versus digital image -- What do dental X-ray images reveal? -- Intraoral imaging techniques -- Intraoral technique errors -- Extraoral imaging techniques -- Quality assurance -- Infection control -- Occupational radiation exposure monitoring -- Hand-held X-ray systems -- Localization of objects (slob rule) -- Recommendations for interpreting images -- X-ray puzzles : spot the differences -- Radiographic anatomy -- Dental caries -- Dental anomalies -- Osseous pathology (alphabetic) -- Lagniappe (miscellaneous oddities).Radiography, DentalRadiography, Dental617.6/07572Hubar J. Sean(Jack Sean),1954-1217275DLCDLCDLCBOOK9910270941103321Fundamentals of oral and maxillofacial radiology2815292UNINA