Presentation on theme: "Dr. Alexandra I. Cristea CS 411: Dynamic Web-Based Systems Exam Preparation."— Presentation transcript:
Dr. Alexandra I. Cristea CS 411: Dynamic Web-Based Systems Exam Preparation
2 Exam Structure Time allowed: 2 hours This is a closed book exam. No information sources and communication devices are allowed. Illegible text will not be evaluated. Answer THREE questions (out of FOUR topics). –Each 33 marks, for a total of 99 marks (plus 1 mark for good reasoning). This will represent 50% of your overall mark (the rest of 50% is coursework & presentation) Read carefully the instructions on the answer book and make sure that the particulars required are entered on each answer book. Day, Time, Place: MAY 24 th in F107 (engineering) at 13:45 (starting around 2pm). –Check exam time-table for changes! tions/ tions/
3 Exam 2012/13 topics 1.Adaptive Hypermedia, Personalisation in e- commerce, User Modelling 2.Authoring of Adaptive Systems, LAOS, LAG framework, LAG language 3.Semantic Web, RDF, RDFS, OWL, SPARQL 4.Social Web for the Adaptive Web: Web 2.0, Social Web, Collaborative Filtering
4 General info check old exams online.old exams online
5 1. Adaptive Hypermedia, User Modelling and Personalisation What do I need to know?
6 AH Main text:Adaptive Hypermedia;Adaptive Hypermedia Also read: AH; Adaptive Education; AdaptiveContentPresentation.pdf; AdaptiveNavigationSupport.pdf; OpenCorpusAEH.pdf; Privacy- EnhancedWebPersonalization.pdf; UsabilityEngineeringforAdaptiveWeb.pdf PersonalizationECommerce.pdfAHAdaptive Education AdaptiveContentPresentation.pdf AdaptiveNavigationSupport.pdf OpenCorpusAEH.pdfPrivacy- EnhancedWebPersonalization.pdf UsabilityEngineeringforAdaptiveWeb.pdf PersonalizationECommerce.pdf Systems: ADE; GALE; AHA! AH Example Systems, as well as in the papers aboveADEGALEAHA! AH Example Systems –Only from the p.o.v of algorithms, functionality – not to remember by heart how each works
UM Main text: User Modelling;User Modelling Also read: Generic-UM.pdf; UM.pdf; UserProfilesforPersonalizedInfoAccess. pdf; AH and Cognitive Styles; Personalisation and Cognitive StylesGeneric-UM.pdfUM.pdf UserProfilesforPersonalizedInfoAccess. pdfAH and Cognitive Styles Personalisation and Cognitive Styles ILS questionnaire –Axes, what they represent –Not all questions by heart! 7
8 Adaptive Hypermedia Why, areas of application, what to adapt, Brusilovskys taxonomy, Adapt to what, (UM, GM, DM, Envir.) how to adapt, Brusilovskys loop, adaptability versus adaptivity, new solutions. You can be presented with a description of an application, and asked to describe it in terms of AH as above. E.g., what is Amazon book recommendation adapting to? What is being adapted? Etc.
9 (Personalization in e-Commerce)** Benefits, perspectives, ubiquitous computing, b2b, b2c, CRM, CDI, pull, push, generalized, personalised recommendations, hybrid, latency (cold start), m- commerce Again, theory and application of theory in practice; e.g., a business personalization case is presented to you, and you are asked to describe it in terms of the newly learned acronyms and give the definitions. You would need to recognize from the description which apply and which not. E.g., is Amazons book recommender technique push or pull? Is b2b, b2c? Etc.
10 User Modeling What, why, what for, how, early history, academic developments, what can we adapt to (revisited, extended – knowledge, cognitive, etc.), generic UM techniques, new developments Stereotypes, overlays, UM system, UM shell services + requirements (Kobsa), semantic levels of UM, deep- shallow UM, cognitive styles – Kolb, field-dep-indep, intended/keyhole/obstructed plan recognition, moods and emotions, preferences UM techniques: rule-based, frame-based, network- based, probability, DT, sub-symbolic, example-based Challenges for UM UM server + requirements
11 User Modeling Theory + application thereof either on a system you know, or on a system with a given description; e.g., is Amazon book recommendation based on UM shell services, or UM server – plus justification! Or: how would you extend the recommendation to cater for Kolb taxonomys active people?
12 What do I need to know? 2. Authoring of Adaptive Systems, LAOS, LAG framework, LAG language
Main texts: Authoring of AH ; Frameworks for AH authoring: LAOS; LAG model;Authoring of AH Frameworks for AH authoring: LAOSLAG model Also read: original LAOS paper; extensions; Authoring system examples, demos ((best)papers 09, 08, 06, 05); System: Authoring system (MOT4)original LAOS paperextensions Authoring system examples, demos Authoring system (MOT4) –What kind of adaptation, theory behind it,.. –Not every instruction 13 Authoring of Adaptive Systems, LAOS, LAG framework
LAG Main text: Adaptation languages: LAG language; Also see: ADE demo page; (demos (LAG, description, CAF, AHA! demo: select anonymous session!); GALE)Adaptation languages: LAG language;ADE demo pagedemos (LAG, description, CAF, AHA! demo: select anonymous session!)GALE 14
15 Authoring of Adaptive Systems, LAOS, LAG framework What is specific to authoring of AH? Content alternatives, UM descript, presentation, adaptation tech., roles LAOS components and justification, LAG model layers and justification,
16 LAG LAG language : a small program – either to read or to write !! (based on programs youve been shown, and programs youve been asked to create for the coursework)
3. Semantic Web, RDF, RDFS, OWL, SPARQL What do I need to know? 17
Semantic Web, RDF Main texts: Semantic Web for the Adaptive Web; SW: RDFSemantic Web for the Adaptive WebSW: RDF Also read: IFETS-journal-paper;IFETS-journal-paper 18
OWL Main texts: SW: OWL Also as OWL video lectureMain texts: SW: OWLOWL video lecture 19
SPARQL SPARQL lecture and notes Also read: Online video lecture SPARQL; (SPARQL ppt old lecture slides going with the video) Extra lecture material: READING GUIDE; SW: SPARQL to be read online); online testingOnline video lecture SPARQLSPARQL ppt old lecture slides going with the videoREADING GUIDESW: SPARQL online testing –You may read about additional commands, but only what was taught can appear in the exam, unless defined by the exam question 20
22 RDF RDF: def, purpose, characteristics, syntax, graphical and RDF/XML representations – you should be able to represent your data in RDF; namespaces – why and how in RDF/XML, resource, description, properties as attributes, resources, elements, containers – bag, seq, alt -, collections, reification, RDF Schema – classes, subclasses (long, short-hand notation -), range, domain, type
24 SPARQL SPARQL: what for?; SELECT, CONSTRUCT, ASK, DESCRIBE (you should be able to know the difference between them, and to read/write some simple queries, mainly based on SELECT)
25 4. Social Web for the Adaptive Web: Web 2.0, Social Web, Collaborative Filtering What do I need to know?
Web 2.0 Main text: Web 2.0;Web 2.0 Also read: Towards Adaptation in Learning 2.0and Social Personalized Lifelong LearningTowards Adaptation in Learning 2.0Social Personalized Lifelong Learning 26
Collaborative Filtering Main text: Collaborative Filtering PaperCollaborative Filtering Paper Also read about CF on Topolor; User Guide TopolorTopolorUser Guide Topolor 27
28 Web 2.0, Social Web Web 2.0 definitions, features, applications, related concepts (, folksonomy, user generated content, user types/roles, collaborative creation/sharing, bottom-up vs. top-down, emerging communities, could tag, mashups, blogs/micro-blogs ), example systems, OReilly principles, implications, aspects of collaboration, Changes for Marketers, RSS, social/business/technology trends, AJAX, SOA, Adaptation/User Profile in Web 2.0 (explicit-implicit data collection) You can be asked theory questions, you can be asked to discuss the topics, you can be asked how a given system fairs in term of the theory youve learned
29 Web 2.0, Social Web, Collaborative Filtering content-based filtering (items, grouping, rating, accuracy), collaborative filtering (automatic; rating patterns; sharing; advantages – disadvantages; passive-active; explicit-implicit; first-rater; cold- start), hybrid filtering, group recommendations, social filtering (similarity computations) ; algorithms (at least to have understood and be able to discuss, not learn by heart formulas) You can be asked theory questions, you can be asked to discuss the topics, you can be asked how a given system fairs in term of the theory youve learned
Q&A Will each topic contain questions from all subjects mentioned in the title? –No. They represent the pool of information from which questions are asked. See also last years exam. How long should I spend on one topic? –Max around 40 min (40*3=120min = 2 hours) Why are there differences in structure to last year? –Regulations. Because you are given more choice, and because its considered better for the exam paper to be close to 100 marks. We are marked up to 100 marks. The previous year was marked up to 50. Are our questions more difficult? –No. The question weight and difficulty will take into account the percentage of the work to your overall mark (50%). 31