SubjectsSubjects(version: 850)
Course, academic year 2019/2020
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Customer preferences - NDBI021
Title in English: Zákaznické preference
Guaranteed by: Department of Software Engineering (32-KSI)
Faculty: Faculty of Mathematics and Physics
Actual: from 2019 to 2019
Semester: summer
E-Credits: 4
Hours per week, examination: summer s.:2/1 C+Ex [hours/week]
Capacity: unlimited
Min. number of students: unlimited
State of the course: taught
Language: Czech, English
Teaching methods: full-time
Additional information: http://www.ksi.mff.cuni.cz/~vojtas/vyuka/vyuka.html
Note: enabled for web enrollment
Guarantor: prof. RNDr. Peter Vojtáš, DrSc.
Class: Informatika Mgr. - Softwarové systémy
Classification: Informatics > Database Systems, Software Engineering
Annotation -
Last update: RNDr. Michal Kopecký, Ph.D. (09.05.2019)
We consider the challenge of implementing e-shop search configuration (e.g. a car – price, consumption, HP … from-to). Due to requirements of business it DOES matter how the selection conditions are implemented – relaxed, aggregated. Via machine learning can our model be adapted to different customers. We do not deal with technologies, rather we learn to create and evaluate preference models based on customer behavior, web content extraction and effectively find top-k answers. Labs are composed of preference learning and a project of a virtual Lean Startup.
Course completion requirements -
Last update: RNDr. Michal Kopecký, Ph.D. (09.05.2019)

Terms of passing the course consist of homework, mainly on paper and some about experimenting with small illustrative data. These are only conditions for getting credits. Exam is oral and requires basic understanding of the whole material.

As soon as terminology is introduced, detailed milestones (also form of deliverables) and preferred deadlines (with possible repeated attempts) will be announced at a lab. There is no evidence on personal presence. Nevertheless, no additional explanation for tasks will be given, except on the respective lab and brief description on the course web. Final deadline is end of semester.

Literature -
Last update: RNDr. Michal Kopecký, Ph.D. (09.05.2019)
  • Fagin, Lotem, Naor. Optimal aggregation algorithms for middleware, J. Computer and System Sciences 66 (2003), pp. 614-656
  • Eric Ries. The Lean Startup: How Today's Entrepreneurs Use Continuous Innovation to Create Radically Successful Businesses. Crown Business 2011
  • Abiteboul S., Hull R., Vianu V.: Foundations of Databases, Addison-Wesley 1995
  • W3C standards,
  • D. Harel, D. Kozen and J. Tiuryn. Dynamic Logic, MIT Press, Cambridge, MA, USA ©2000
  • Various readings from: IBM T-shape education; Big Five personality traits; Does Personality Matter on Consumer Behavior?; Peterson-JB-Maps-of-Meaning-Routledge-1999; ...

Requirements to the exam -
Last update: RNDr. Michal Kopecký, Ph.D. (09.05.2019)

Are on the Web with classroom content, link will be announced for enrolled students only.

Syllabus -
Last update: RNDr. Michal Kopecký, Ph.D. (09.05.2019)
Modeling customer preferences
Introductions, motivation, challenges and use cases of customer preferences, Lean startup model,

pLMPM - partly Linear Monotone Preference Model, uniqueness of LMPM representation, tableaux representation, query inclusion, formal challenge-response framework

Top-k algorithms - querying/searching with preferences
Fagin monotone model of customer preferences, RDF data and threshold algorithm without RA

Theoretical optimality of threshold algorithm, indexes for a multi-customer model

Learning web data extractors and customer preferences
Learning (acquisition) of customer preference, various metrics for evaluation the quality of models

Standards for description of web resources and knowledge graphs

Extension of web standards with dynamic logic

Dynamic web semantization in support of customer preference modeling quality

Preferential Datalog
Preferential logic as a language for modeling of preferences, many valued modus pones and its correctness

Procedural and declarative semantics of preferential Datalog without negation and with recursion, correctness

Fixpoint for preferential Datalog and computability of the minimal model, approximate completeness

Challenge-response framework
Challenge-response situations in software engineering, algorithmic complexity and formal modeling

Current achievements in the field

 
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