Publications
Authors:
  • Boris Chidlovskii
Citation:
ICML, International Conference on Machine Learning, Lille, France, 6-11 July, 2015.
Abstract:
We analyze the work of urban trip planners and
the relevance of trips they recommend upon user
queries. We propose to improve the planner recommendations
by learning from choices made
by travelers who use the transportation network
on the daily basis. We analyze individual travelers’
trips and convert them into pair-wise preferences
for traveling from a given origin to a destination
at a given time point. To address the
sparse and noisy character of raw trip data, we
model passenger preferences with a number of
smoothed time-dependent latent variables, which
are used to learn a ranking function for trips. This
function can be used to re-rank the top planner’s
recommendations. Results of tests for cities of
Nancy, France and Adelaide, Australia show a
considerable increase of the recommendation relevance
Year:
2015
Report number:
2015/054
Attachments: