Dr. Alexandra I. Cristea

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CS 411 Dynamic Web-Based SystemsExam
Preparation

• Dr. Alexandra I. Cristea
• http//www.dcs.warwick.ac.uk/acristea/

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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 0930 Panorama Room
• Check exam time-table for changes!
  

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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

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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.

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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

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1. 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.

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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 Amazons book recommender technique push
  or pull? Is b2b, b2c? Etc.

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2. User Modelling

• Texts Generic-UM.pdf UM.pdf UserProfilesforPers
  onalizedInfoAccess.pdf

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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

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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 taxonomys
  active people?

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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!)

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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 youve been
  shown, and programs youve been asked to create
  for the coursework)

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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

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4. Semantic Web, RDF, SPARQL, OWL

• SW inventor, sytactic vs SW, ontology def., SW
  ontology languages, Layer Cake

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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

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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, necsuff., reasoning,
  ontology extension,

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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)

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5. Social Web, Collaborative Filtering

• Texts RecommendationGroups.pdf
  AdaptiveSupportDistributedCollaboration.pdf
  HybridWebRecommenderSystems.pdf
  CollaborativeFiltering.pdf

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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 youve
  learned

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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

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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

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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

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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

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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

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• Questions?