Conditional Preference Learning for Personalized and Context-Aware Journey Planning

Haqqani, M, Ashrafzadeh, A, Li, X and Yu, X 2018, 'Conditional Preference Learning for Personalized and Context-Aware Journey Planning', in Proceedings of the 15th International Conference on Parallel Problem Solving from Nature (PPSN'2018), Coimbra, Portugal, 8 - 12 September 2018, pp. 451-463.


Document type: Conference Paper
Collection: Conference Papers

Title Conditional Preference Learning for Personalized and Context-Aware Journey Planning
Author(s) Haqqani, M
Ashrafzadeh, A
Li, X
Yu, X
Year 2018
Conference name PPSN'2018
Conference location Coimbra, Portugal
Conference dates 8 - 12 September 2018
Proceedings title Proceedings of the 15th International Conference on Parallel Problem Solving from Nature (PPSN'2018)
Publisher Springer International Publishing
Place of publication Berlin
Start page 451
End page 463
Total pages 13
Abstract Conditional preference networks (CP-nets) have recently emerged as a popular language capable of representing ordinal preference relations in a compact and structured manner. In the literature, CP-nets have been developed for modeling and reasoning in mainly toy-sized combinatorial problems, but rarely tested in real-world applications. Learning preferences expressed by passengers is an important topic in sustainable transportation and can be used to improve existing journey planning systems by providing personalized information to the passengers. Motivated by such needs, this paper studies the effect of using CP-nets in the context of personalized and context-aware journey planning. We present a case study where we learn to predict the journey choices by the passengers based on their historical choices in a multi-modal urban transportation network. The experimental results indicate the benefit of the conditional preference in passengers' modeling in context-aware journey planning.
Subjects Operations Research
Neural, Evolutionary and Fuzzy Computation
Pattern Recognition and Data Mining
DOI - identifier 10.1007/978-3-319-99253-2_36
Copyright notice © Springer Nature Switzerland AG 2018
ISBN 9783319992532
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