1. Dr. Alexandra I. Cristea http://www.dcs.warwick.ac.uk/~acristea/ CS 411: Dynamic Web-Based Systems Exam Preparation

2. 2 Exam Structure Time allowed: 3 hours This is a closed book exam. No information sources and communication devices are allowed. Illegible text will not be evaluated. Answer FOUR questions (out of SIX). –Each 25 marks, for a total of 100 marks. This will represent 70% of your overall mark (the rest of 30% 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: 22 MAY; 09:30; Panorama Room –Check exam time-table for changes!

3. 3 Exam topics 1.Adaptive Hypermedia, Personalization in e- Commerce 2.User Modelling 3.Authoring of Adaptive Systems, LAOS, LAG framework, LAG language 4.Semantic Web, RDF, SPARQL, OWL 5.Social Web, Collaborative Filtering 6.Adaptive Focused Crawling, Data Mining, Personalized Search, Privacy Enhanced Web Personalization

4. 4 General info New exam, But: content overlap exists with CS253 module and exam. Especially for topics Semantic Web, OWL and RDF, check the old exams of CS253.old exams of CS253

5. 5 1. Adaptive Hypermedia, Personalization in e-CommerceAdaptive Hypermedia Personalization in e-Commerce Texts: AH: AdaptiveContentPresentation.pdf; AdaptiveNavigationSupport.pdf; OpenCorpusAEH.pdf; Privacy- EnhancedWebPersonalization.pdf; UsabilityEngineeringforAdaptiveWeb.p dfAdaptiveContentPresentation.pdf AdaptiveNavigationSupport.pdf OpenCorpusAEH.pdfPrivacy- EnhancedWebPersonalization.pdf UsabilityEngineeringforAdaptiveWeb.p df P in eC: PersonalizationECommerce.pdfPersonalizationECommerce.pdf

6. 6 1. Adaptive HypermediaAdaptive Hypermedia Why, areas of application, what to adapt,,Brusilovsky’s taxonomy, Adapt to what, (UM, GM, DM, Envir.) how to adapt, Brusilovsky’s 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.

7. 7 1. Personalization in e- CommercePersonalization 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 Amazon’s book recommender technique push or pull? Is b2b, b2c? Etc.

8. 8 2. User ModellingUser Modelling Texts: Generic-UM.pdf; UM.pdf; UserProfilesforPersonalizedInfoAccess.pdf ;Generic-UM.pdfUM.pdf UserProfilesforPersonalizedInfoAccess.pdf

9. 9 2. User ModellingUser Modelling 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, filed-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

10. 10 2. User ModellingUser Modelling 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 taxonomy’s active people?

11. 11 3. Authoring of Adaptive Systems, LAOS, LAG framework, LAG languageAuthoring of Adaptive SystemsLAOSLAG frameworkLAG language Texts: WWWconfPaper; IFETS-journal- paper; Authoring system examples, demosWWWconfPaperIFETS-journal- paperAuthoring system examples, demos Demos: demos (LAG, description, CAF, AHA! demo: select anonymous session!)demos (LAG, description, CAF, AHA! demo: select anonymous session!)

12. 12 3. Authoring of Adaptive Systems, LAOS, LAG framework, LAG languageAuthoring of Adaptive SystemsLAOSLAG frameworkLAG language What is specific to authoring of AH? Content alternatives, UM descript, presentation, adaptation tech., roles LAOS components and justification, LAG model layers and justification, LAG language : a small program – either to read or to write !! (based on programs you’ve been shown, and programs you’ve been asked to create for the coursework)

13. 13 4. Semantic Web, RDF, SPARQL, OWLSemantic WebRDF SPARQLOWL Texts: READING GUIDE; SW: SPARQL (to be read online); online testingREADING GUIDESW: SPARQL online testing Some extra courses to visit: –RDF course ; video;RDF course video –OWL course ; video;OWL course video –SPARQL course ; video;SPARQL course video

14. 14 4. Semantic Web, RDF, SPARQL, OWLSemantic WebRDF SPARQLOWL SW: inventor, sytactic vs SW, ontology def., SW ontology languages, ‘Layer Cake’

15. 15 4. Semantic Web, RDF, SPARQL, OWLSemantic WebRDF SPARQLOWL RDF: def, purpose, 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

16. 16 4. Semantic Web, RDF, SPARQL, OWLSemantic WebRDF SPARQLOWL OWL: def, purpose, sublanguages, individuals, object properties (domain, range from RDF), restrictions on prop. (allValuesFrom, someValuesFrom, hasValue, minCardinality, maxCardinality, cardinality), inverse prop., trans. Prop., sub-prop., datatype prop., owl classes – disjoint, enumerated classes - oneOf, intersectionOf, complementOf, unionOf, class Conditions – necessary, nec+suff., reasoning, ontology extension,

17. 17 4. Semantic Web, RDF, SPARQL, OWLSemantic WebRDF SPARQLOWL SPARQL: what for?; SELECT, CONSTRUCT, ASK, DESCRIBE (you should be able to know the difference between them, and to read some simple queries, mainly based on SELECT)

18. 18 5. Social Web, Collaborative FilteringSocial Web Texts: RecommendationGroups.pdf; AdaptiveSupportDistributedCollaboration.p df; HybridWebRecommenderSystems.pdf ; CollaborativeFiltering.pdfRecommendationGroups.pdf AdaptiveSupportDistributedCollaboration.p dfHybridWebRecommenderSystems.pdf CollaborativeFiltering.pdf

19. 19 5. Social Web, Collaborative FilteringSocial Web Web 2.0, user profiling (explicit-implicit data collection), 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) 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 you’ve learned

20. 20 6. Adaptive Focused Crawling, Data Mining, Personalized Search, Privacy Enhanced Web PersonalizationAdaptive Focused Crawling Data MiningPersonalized SearchPrivacy Enhanced Web Personalization These are topics based on the last topic, crawling, and your presentations. grouped together. Your main source for the group presentations should be the text (literature). Texts: AdaptiveFocusedCrawling.pdf ; DataMining.pdf ; PersonalizedSearch.pdf; Privacy-EnhancedWebPersonalization.pdfAdaptiveFocusedCrawling.pdf DataMining.pdfPersonalizedSearch.pdf Privacy-EnhancedWebPersonalization.pdf

21. 21 6. Adaptive Focused Crawling, Data Mining, Personalized Search, Privacy Enhanced Web PersonalizationAdaptive Focused Crawling Data MiningPersonalized SearchPrivacy Enhanced Web Personalization Crawling: on the WWW, focused c. (adaptive or not; dark matter, page sets: In, Out, SCC, deep web; strategies – BF, Backlink, PageRank, HITS, fish, tunneling, etc.), agent-based (genetic, ants), ML (statistical model), eval. Methods (time, precision, recall) Theory + discussion & interpretation Small problems/ numerical computations based on theory

22. 22 6. Adaptive Focused Crawling, Data Mining, Personalized Search, Privacy Enhanced Web PersonalizationAdaptive Focused Crawling Data MiningPersonalized SearchPrivacy Enhanced Web Personalization Data mining: def, cycle, collection, preprocessing (+ tasks, web-usage, fusion, cleaning, pageview identification, sessionization, episode id, ), modelling (offline, clustering, rule discovery, sequential models, LVM; hybrids), representation, data sources, recommendations, evaluations Theory + discussion & interpretation

23. 23 6. Adaptive Focused Crawling, Data Mining, Personalized Search, Privacy Enhanced Web PersonalizationAdaptive Focused Crawling Data MiningPersonalized SearchPrivacy Enhanced Web Personalization Personalised Search: def, surf, query, content/collaborative-based (polysemy, synonymy), user modeling, profiling, re-ranking, query modification, relevance feedback, query expansion, contextualised, search histories, agents, offline-online, rich representations (frames, AI, UM, stereotypes, feedback), collaborative search (similarity, statistics, communities), adaptive result clustering, hyperlink-based personalisation, combined approaches Theory + discussion & interpretation

24. 24 6. Adaptive Focused Crawling, Data Mining, Personalized Search, Privacy Enhanced Web PersonalizationAdaptive Focused Crawling Data MiningPersonalized SearchPrivacy Enhanced Web Personalization Privacy-enhanced Web personalisation: concerns (personalisation vs. privacy; methods, effects, differences), factors (knowledge, trust, benefits, costs, hyperbolic temporal discounting, ), laws (on what?; EU?; ACM list of recommendations), technology (pseudonymous, anonymous, client-side, centralised, issues, perturbation/ obfuscation, personalising privacy) Theory + discussion & interpretation

25. 25 Questions?