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.
Last update: Zavoral Filip, RNDr., Ph.D. (27.04.2021)
V předmětu se zaměříme především na hlubší pochopení uživatelských preferencí/potřeb/požadavků.
Problematika je závislá např. na frekvenci návštěv a cílové doméně. Zaměříme se např. na problémy
proporcionality, změny preferencí, multikriterialitu, nestranného vyhodnocování a dále na algoritmy schopné se v
takových podmínkách učit a doporučovat. Dále se zaměříme na širší kontext interpretace preferencí například při
vyhledávání (ostré/fuzzy preference, grafická interpretace preferencí). Cvičení se skládají z referátů o současných
výsledcích a projektu virtuálního „Lean startup“.
Last update: Zavoral Filip, RNDr., Ph.D. (27.04.2021)
Literature -
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
Abiteboul S., Hull R., Vianu V.: Foundations of Databases, Addison-Wesley 1995
Klement E.P, Mesiar R., Pap E.: Triangular norms, Springer 2000
Last update: Peška Ladislav, Mgr., 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
