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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Notes
- 1.
- 2.
The General Transit Feed Specification (GTFS) data which defines a common format for public transportation schedules and associated geographic information. For more information, please visit http://www.transitwiki.org.
References
Allen, T.E.: CP-nets: from theory to practice. In: Walsh, T. (ed.) ADT 2015. LNCS, vol. 9346, pp. 555–560. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-23114-3_33
Bell, P., Knowles, N., Everson, P.: Measuring the quality of public transport journey planning. In: IET and ITS Conference on Road Transport Information and Control, RTIC 2012, pp. 1–4. IET (2012)
Bonsall, P.: Do we know whether personal travel planning really works? Transp. Policy 16(6), 306–314 (2009)
Boutilier, C., Brafman, R.I., Hoos, H.H., Poole, D.: Reasoning with conditional ceteris paribus preference statements. In: UAI, pp. 71–80 (1999)
Burges, C., et al.: Learning to rank using gradient descent. In: ICML, pp. 89–96 (2005)
Corder, G.W., Foreman, D.I.: Nonparametric Statistics: A Step-by-Step Approach. Wiley, Hoboken (2014)
Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.A.M.T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6(2), 182–197 (2002)
Haqqani, M., Li, X.: An evolutionary approach for learning conditional preference networks from inconsistent examples. In: Cong, G., Peng, W.-C., Zhang, W.E., Li, C., Sun, A. (eds.) ADMA 2017. LNCS, vol. 10604, pp. 502–515. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-69179-4_35
Haqqani, M., Li, X., Yu, X.: Estimating passenger preferences using implicit relevance feedback for personalized journey planning. In: Wagner, M., Li, X., Hendtlass, T. (eds.) ACALCI 2017. LNCS, vol. 10142, pp. 157–168. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-51691-2_14
Haqqani, M., Li, X., Yu, X.: An evolutionary multi-criteria journey planning algorithm for multimodal transportation networks. In: Wagner, M., Li, X., Hendtlass, T. (eds.) ACALCI 2017. LNCS, vol. 10142, pp. 144–156. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-51691-2_13
Herbrich, R., Graepel, T., Obermayer, K.: Support vector learning for ordinal regression. In: ICANN, vol. 1, pp. 97–102 (1999)
Herlocker, J.L., Konstan, J.A., Terveen, L.G., Riedl, J.T.: Evaluating collaborative filtering recommender systems. ACM Trans. Inf. Syst. (TOIS) 22(1), 5–53 (2004)
Kazawa, H., Hirao, T., Maeda, E.: Order SVM: a kernel method for order learning based on generalized order statistics. Syst. Comput. Jpn. 36(1), 35–43 (2005)
Liu, J., Xiong, Y., Caihua, W., Yao, Z., Liu, W.: Learning conditional preference networks from inconsistent examples. IEEE TKDE 26(2), 376–390 (2014)
Liu, J., Yao, Z., Xiong, Y., Liu, W., Caihua, W.: Learning conditional preference network from noisy samples using hypothesis testing. Knowl.-Based Syst. 40, 7–16 (2013)
Owen, N., Humpel, N., Leslie, E., Bauman, A., Sallis, J.F.: Understanding environmental influences on walking. Am. J. Prev. Med. 27(1), 67–76 (2004)
Spearman, C.: The proof and measurement of association between two things. Am. J. Psychol. 15(1), 72–101 (1904)
Xu, J., Li, H.: AdaRank: a boosting algorithm for information retrieval. In: ACM SIGIR, pp. 391–398 (2007)
Acknowledgment
This research was supported under Australian Research Council’s Linkage Projects funding scheme (project number LP120200305).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Haqqani, M., Ashrafzadeh, H., Li, X., Yu, X. (2018). Conditional Preference Learning for Personalized and Context-Aware Journey Planning. In: Auger, A., Fonseca, C., Lourenço, N., Machado, P., Paquete, L., Whitley, D. (eds) Parallel Problem Solving from Nature – PPSN XV. PPSN 2018. Lecture Notes in Computer Science(), vol 11101. Springer, Cham. https://doi.org/10.1007/978-3-319-99253-2_36
Download citation
DOI: https://doi.org/10.1007/978-3-319-99253-2_36
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-99252-5
Online ISBN: 978-3-319-99253-2
eBook Packages: Computer ScienceComputer Science (R0)