SubjectsSubjects(version: 837)
Course, academic year 2018/2019
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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 2017
Semester: summer
E-Credits: 5
Hours per week, examination: summer s.:2/2 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. (10.05.2017)
We are interested in the process which governs customer’s interface action and system response of an e-shop. We learn: to create and evaluate customer preference models based on some business models; to effectively find top-k answers; a domain calculi for these. Labs are composed of reporting on current achievements, preference learning, a project of a virtual Lean Startup and customer imitation via a social network.
Course completion requirements
Last update: prof. RNDr. Peter Vojtáš, DrSc. (14.03.2018)

can be found on http://www.ksi.mff.cuni.cz/~vojtas/vyuka/y5v/NDBI021/021TermsPassingCourseGeneral.html

Literature -
Last update: T_KSI (26.04.2016)
  • A comparison of fuzzy and annotated logic programming, Fuzzy Sets and Systems, 144 (2004) 173-192
  • 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
  • G. James, D. Witten, T. Hastie, R. Tibshirani. An Introduction to Statistical Learning with Applications in R. Springer 2013

Syllabus -
Last update: RNDr. Michal Kopecký, Ph.D. (10.05.2017)

Modeling customer preferences and querying with preferences

Introduction, motivation, challenges and use cases of customer preferences,

Lean startup model,

LMPM - Linear Monotone Preference Model, uniqueness of LMPM representation.

Top-k algorithms - querying/searching with preferences

Fagin’s monotone model of customer preferences and algorithms for computing top-k.

Theoretical optimality of threshold algorithm and practical experiments, a multi-customer model

Learning customer preferences

Problem of learning (acquisition) of customer preference

Learning customer preferences

Various metrics for evaluation the quality of models

Formal framework for transferability of preference models, connections to economical and optimization models

Mathematical Fuzzy Datalog - 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

Theorem on approximate completeness of preferential Datalog

 
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