Constructing CP-Nets from Users Past Selection
| Autor: | Reza Khoshkangini, Francesca Rossi, Maria Silvia Pini |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Computer science
business.industry Bayesian network Context (language use) 02 engineering and technology Construct (python library) Recommender system Machine learning computer.software_genre 01 natural sciences Domain (software engineering) 010104 statistics & probability 0202 electrical engineering electronic engineering information engineering Selection (linguistics) 020201 artificial intelligence & image processing Artificial intelligence 0101 mathematics business computer |
| Zdroj: | AI 2019: Advances in Artificial Intelligence ISBN: 9783030352875 Australasian Conference on Artificial Intelligence |
| DOI: | 10.1007/978-3-030-35288-2_11 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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