Tech 1. May 18th 2018. Data Driven Innovation 2018. Engineering Department, University of Roma Tre

1. Global Heat Map &
Learning to Rank
Marco Catalano
Lead Engineer - Here Technologies
Data Driven Innovation – Rome 2018

2. Global Heat Map & Learning to Rank | Data Driven Innovation – Rome 20182 © 2018 HERE Technologies
Agenda
01. Reverse Geocoder and
Global Heat Map
01.1 Reverse Geocoder
01.2 Extract data from the logs
01.3 Global Heat Map
02. Autocompletion and
Learning to Rank
02.1 Autocompletion
02.2 Learning to Rank
02.3 Continuos Ascending

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01
Reverse Geocoder and
Global Heat Map

4. Global Heat Map & Learning to Rank | Data Driven Innovation – Rome 20184 © 2018 HERE Technologies
Reverse Geocoder
Reverse Geocoding is the process
of obtaining an address, an
administrative area or a known
landmark from a location in form
of its geocoordinates (Lat Lon)
The Reverse Geocoder is one of
HERE Technologies most heavily
used services and a huge amount
of data is produced by its access
logs

5. Global Heat Map & Learning to Rank | Data Driven Innovation – Rome 20185 © 2018 HERE Technologies
What can we do with this information?
The basic consideration is that if a lot of reverse geocoder requests for a certain location are
present than there is a high likelihood that the location itself is to be considered a relevant one
defining a notion of relative place importance
The next step is therefore to count the reverse geocode probes in respect to some cellulation of
the earth
The earth is partitioned into cells
Defined by fixed latitude &
longitude increments

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Aggregate the Request Coordinates
Reverse geocodes within each cell are
summed and the values aggregated into
the bottom left corner of the respective
bounding box

7. Global Heat Map & Learning to Rank | Data Driven Innovation – Rome 20187 © 2018 HERE Technologies
Problems and Limitations
India
Europe
Cells area vary by latitude and longitude
0,0001 degrees of latitude (4 digit
precision) equals to:
• Around 11 m at the equator
• Around 8 m at 67 (N/S)
Longitude and Elevation also introduce a
small error

8. Global Heat Map & Learning to Rank | Data Driven Innovation – Rome 20188 © 2018 HERE Technologies
Visualize the data -> Global Heat Map
Heat map is a two dimensional visual representations of data.
The information, in the form of individual values, is contained
in a matrix and is represented using color gradients.
They are particularly useful where large volumes of data have
to be made comprehensible.
In digital image processing terms a heat map is a spatial
histogram, in other words the aggregation of histograms
computed for each and every earth cell.

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Visualize & Compare

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Visualize & Compare
San Francisco
2016 2014

11. Global Heat Map & Learning to Rank | Data Driven Innovation – Rome 201811 © 2018 HERE Technologies
Visualize & Compare
Frankfurt am Main
2016 2014

12. Global Heat Map & Learning to Rank | Data Driven Innovation – Rome 201812 © 2018 HERE Technologies
Visualize & Compare
Berlin
2016 2014

13. Presentation title | Month 00, 201613 © 2016 HERE | HERE Internal Use Only
02
Autocompletion and
Learning to Rank

14. Global Heat Map & Learning to Rank | Data Driven Innovation – Rome 201814 © 2018 HERE Technologies
Auto Completion
Auto Completion allows end users to get good results with fewer keystrokes.
Use Cases
• Speed up typing
User starts typing and after a few characters a user interface provides a list of suggestions. The user selects what he’s interested in..
• User is unsure about spelling of an address
A user receives immediate feedback relative to input in form of suggestions and can complete or correct input quickly based on the suggestions.
• User is unsure about details of address
“via mazzini verona” suggests a complete address with all its details and can be completed to “Verona, Via Giuseppe Mazzini”

15. Global Heat Map & Learning to Rank | Data Driven Innovation – Rome 201815 © 2018 HERE Technologies
Can we also use the relative place importance to improve our
autocompletion ranking model?
Information retrieval problem
Documents
D = { d1 , d 2 , ... , d N }
Query Q
Retrieval System
Ranked results
{ dq1 , dq2 , ... , dqN }
Tf-idf or BM25

16. Global Heat Map & Learning to Rank | Data Driven Innovation – Rome 201816 © 2018 HERE Technologies
Learning to Rank information retrieval
Documents
D = { d1 , d 2 , ... , d n }
Query Q
Retrieval System
Ranked results
{ dq1 , dq2 , ... , dqn }
Learning Model
Training data
Q1{ dq1-1 , dq1-2 , ... , dq1-n }
Q2{ dq2-1 , dq2-2 , ... , dq2-n }
……..
Qm{ dqm-1 , dqm-2 , ... , dqm-n }

17. Global Heat Map & Learning to Rank | Data Driven Innovation – Rome 201817 © 2018 HERE Technologies
How the Learning model works
f (d, q) -> v Feature
Array of computed features
[V1, V2, … Vn] -> Vf
Ranking function
g (Vf) -> Ranking score
Query-independent depend only on the document
Query-dependent depend on the document and on the
query
Query features depend only on the query

18. Global Heat Map & Learning to Rank | Data Driven Innovation – Rome 201818 © 2018 HERE Technologies
Coordinate Ascent
list-wise linear model which uses coordinate ascent to optimize
is an optimization that optimizes multivariate objective functions by
sequentially doing optimization in one dimension at a time. It cycles through
each parameter and optimizes over it while fixing all the others.
Why?
• It works well enough
• Linear therefore more predictable and human understandable
• Fast training process

19. Presentation title | Month 00, 201619 © 2016 HERE | HERE Internal Use Only
Thank you
Contact
Marco Catalano
Lead Engineer
HERE Technologies
Am Kronberger Hang 8
65824 Schwalbach
50° 9' 43" N. 8° 32' 1" E
Direct line: +4961965866540
marco.catalano@here.com
https://developer.here.com