Constructing CP-Nets from Users Past Selection
2019 (English)In: Lecture Notes in Computer Science: Volume 11919 LNAI, Springer, 2019, p. 130-142Conference paper, Published paper (Refereed)
Abstract [en]
Although recommender systems have been significantly developed for providing customized services to users in various domains, they still have some limitations regarding the extraction of users’ conditional preferences from their past selections when they are in a dynamic context. We propose a framework to automatically extract and learn users’ conditional and qualitative preferences in a gamified system taking into consideration the players’ past behaviour, without asking any information from the players. To do that, we construct CP-nets modeling users preferences via a procedure that employs multiple Information Criterion score functions within an heuristic algorithm to learn a Bayesian network. The approach has been validated experimentally in the challenge recommendation domain in an urban mobility gamified system. © 2019, Springer Nature Switzerland AG.
Place, publisher, year, edition, pages
Springer, 2019. p. 130-142
Series
Lecture Notes in Computer Science, ISSN 0302-9743 ; 11919
Keywords [en]
Bayesian network, CP-net, Gamification, Recommender system, Artificial intelligence, Bayesian networks, Heuristic algorithms, Recommender systems, Conditional preferences, Customized services, Dynamic contexts, Information criterion, Score function, Urban mobility, Knowledge based systems
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:hh:diva-41542DOI: 10.1007/978-3-030-35288-2_11ISI: 000927874400011Scopus ID: 2-s2.0-85076540817ISBN: 9783030352875 (print)OAI: oai:DiVA.org:hh-41542DiVA, id: diva2:1391307
Conference
Australasian Joint Conference on Artificial Intelligence, Adelaide, SA, Australia, December 2-5, 2019
2020-02-042020-02-042023-10-05Bibliographically approved