SubjectsSubjects(version: 901)
Course, academic year 2021/2022
Customer preferences - NDBX021
Title: Zákaznické preference
Guaranteed by: Student Affairs Department (32-STUD)
Faculty: Faculty of Mathematics and Physics
Actual: from 2021
Semester: summer
E-Credits: 5
Hours per week, examination: summer s.:2/2 C+Ex [hours/week]
Capacity: unlimited
Min. number of students: unlimited
Virtual mobility / capacity: no
State of the course: taught
Language: Czech
Teaching methods: full-time
Is provided by: NDBI021
Additional information:
Note: enabled for web enrollment
Guarantor: prof. RNDr. Peter Vojtáš, DrSc.
Class: Informatika Mgr. - Softwarové systémy
Classification: Informatics > Database Systems, Software Engineering
Pre-requisite : {NXXX010, NXXX026, NXXX027, NXXX028, NXXX029, NXXX032, NXXX068}
Incompatibility : NDBI021
Interchangeability : NDBI021
Annotation -
Last update: RNDr. Filip Zavoral, Ph.D. (27.04.2021)
This course focus on deeper understanding of user preferences/needs/requirements. The problem depends both on the user seniority, visits frequency or the particular domain in question. We will focus e.g. on proportionality, preference drift, multicriteriality, unbiased evaluation and on algorithms capable to learn and recommend from such data. We will also focus on a wider context of preference interpretation, e.g. during search (strict/fuzzy preference, graphical interpretation). Labs will mainly focus on refering about recent papers and a virtual "Lean startup" project.
Literature -
Last update: Mgr. Ladislav Peška, Ph.D. (01.02.2022)

Abdollahpouri, H., Mansoury, M., Burke, R., Mobasher, B.: The connection between popularity bias, calibration, and fairness in recommendation. RecSys ’20, p. 726–731. ACM (2020).

Bertani, R.M., A. C. Bianchi, R., Costa, A.H.R.: Combining novelty and popularity on personalised recommendations via user profile learning. Expert Systems with Applications 146, 113149 (2020).

Fleder, D., Hosanagar, K.: Blockbuster culture’s next rise or fall: The impact of recommender systems on sales diversity. Manage. Sci. 55(5), 697–712 (2009).

Ge, Y., Zhao, S., Zhou, H., Pei, C., Sun, F., Ou, W., Zhang, Y.: Understanding Echo Chambers in E-Commerce Recommender Systems, p. 2261–2270. ACM (2020).

Garcin, F., Faltings, B., Jurca, R., Joswig, N.: Rating aggregation in collaborative filtering systems. RecSys’09, p. 349–352. ACM (2009).

S Balcar, L Peska: Personalized Implicit Negative Feedback Enhancements for Fuzzy D’Hondt’s Recommendation Aggregations, iiWAS 2020, ACM (2020)

Jannach, D., Ludewig, M., Lerche, L.: Session-based item recommendation in e-commerce: on short-term intents, reminders, trends and discounts. User Modeling and User-Adapted Interaction 27(3), 351–392 (2017).

T. Joachims, A. Swaminathan, and T. Schnabel. Unbiased learning-to-rank with biased feedback. In WSDM’17, pages 781–789. ACM, 2017

Kaminskas, M., Bridge, D.: Diversity, serendipity, novelty, and coverage: A survey and empirical analysis of beyond-accuracy objectives in recommender systems. ACM Trans. Interact. Intell. Syst. 7(1) (2016).

Steck, H.: Calibrated recommendations. RecSys ’18, pp. 154–162. ACM (2018)

Alexandros Karatzoglou, Balázs Hidasi: Deep Learning for Recommender Systems (Tutorial). RecSys 2017: 396-397

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

D. Harel, D. Kozen and J. Tiuryn. Dynamic Logic, MIT Press, Cambridge, MA, USA ©2000

Abiteboul S., Hull R., Vianu V.: Foundations of Databases, Addison-Wesley 1995

Klement E.P, Mesiar R., Pap E.: Triangular norms, Springer 2000

Syllabus -
Last update: Mgr. Ladislav Peška, Ph.D. (01.02.2022)

User preferences

  • Introduction, motivation, challenges and use-cases of user preferences
  • Modelling / expressing user preferences, types of user feedback, Linear Monotone Preference Model
  • Learning user preferences, feedback interpretation, aggregating preferences & fuzzy logic
  • Applications for user preferences, recommender systems, personalized search, challenge-response model

Advanced topics from recommender systems

  • Fairness and proportionality in recommender systems
  • Multicriterial optimization & evaluation in recommender systems
  • Dynamic recommender systems: multi-armed bandits & reinforcement learning
  • Unbiased evaluation, inverse propensity, feedback loops problem
  • Deep learning for heterogeneous recommender systems

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