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

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

  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.

  5. 1. Adaptive Hypermedia, Personalization in e-Commerce • Texts: • AH: AdaptiveContentPresentation.pdf; AdaptiveNavigationSupport.pdf; OpenCorpusAEH.pdf; Privacy-EnhancedWebPersonalization.pdf; UsabilityEngineeringforAdaptiveWeb.pdf • P in eC: PersonalizationECommerce.pdf

  6. 1. Adaptive 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. 1. 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 Amazon’s book recommender technique push or pull? Is b2b, b2c? Etc.

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

  9. 2. User 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. 2. User 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. 3. Authoring of Adaptive Systems, LAOS, LAG framework, LAG language • Texts: WWWconfPaper; IFETS-journal-paper; Authoring system examples, demos • Demos: demos (LAG, description, CAF, AHA! demo: select anonymous session!)

  12. 3. Authoring of Adaptive Systems, LAOS, LAG framework, LAG 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. 4. Semantic Web, RDF, SPARQL, OWL • Texts: READING GUIDE; SW: SPARQL (to be read online); online testing • Some extra courses to visit: • RDF course ; video; • OWL course ; video; • SPARQL course ; video;

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

  15. 4. Semantic Web, RDF, SPARQL, OWL • 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. 4. Semantic Web, RDF, SPARQL, OWL • 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. 4. Semantic Web, RDF, SPARQL, OWL • 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. 5. Social Web, Collaborative Filtering • Texts: RecommendationGroups.pdf; AdaptiveSupportDistributedCollaboration.pdf; HybridWebRecommenderSystems.pdf ; CollaborativeFiltering.pdf

  19. 5. Social Web, Collaborative Filtering • 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. 6. Adaptive Focused Crawling, Data Mining, Personalized Search, Privacy 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.pdf

  21. 6. Adaptive Focused Crawling, Data Mining, Personalized Search, Privacy 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. 6. Adaptive Focused Crawling, Data Mining, Personalized Search, Privacy 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. 6. Adaptive Focused Crawling, Data Mining, Personalized Search, Privacy 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. 6. Adaptive Focused Crawling, Data Mining, Personalized Search, Privacy 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. Questions?