=Paper=
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|storemode=property
|title=None
|pdfUrl=https://ceur-ws.org/Vol-2086/AICS2017_paper_5.pdf
|volume=Vol-2086
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==None==
<pdf width="1500px">https://ceur-ws.org/Vol-2086/AICS2017_paper_5.pdf</pdf>
<pre>
    Personalised Recommendations for Modes of
      Transport: A Sequence-based Approach

           Gunjan Kumar, Houssem Jerbi, and Michael P. O’Mahony

                        Insight Centre for Data Analytics
           School of Computer Science, University College Dublin, Ireland



1     Summarized Publication(s)

                     Personalised Recommendations for Modes of Transport:
Paper Title:
                     A Sequence-based Approach [1]
                     http://www2.cs.uic.edu/∼urbcomp2013/urbcomp2016/
URL:
                     papers/Personalised.pdf
                     The 5th International Workshop on Urban Computing
Conference / Journal
                     held in conjunction with the 22th ACM SIGKDD 2016
Publication Date     August 14, 2016


2     Summary

In this paper we consider the problem of recommending modes of transport to
users in an urban setting. In particular, we build on our past work in which
a general framework for activity recommendation is proposed. To model the
personal preferences and habits of users, the framework uses a sequence-based
approach to capture the order as well as the context associated with user activity
patterns. Here, we extend this work by introducing a machine learning approach
to learn and take into account the natural variations in the regularity and repe-
tition of individual user behaviour that occur. We demonstrate the versatility of
our recommendation framework by applying it to the transport domain, and an
evaluation using a real-world dataset demonstrates the efficacy of the approach.1


References
1. Kumar, G., Jerbi, H., O’Mahony, M.P.: Personalised recommendations for modes
   of transport: A sequence-based approach. The 5th ACM SIGKDD International
   Workshop on Urban Computing (UrbComp 2016) (2016)




1
    This work was supported by Science Foundation Ireland under Grant Number
    SFI/12/RC/2289 through The Insight Centre for Data Analytics.

</pre>