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Global Heat Map & Learning to Rank. Marco Catalano - HERE Technologies

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

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Global Heat Map & Learning to Rank. Marco Catalano - HERE Technologies

  1. 1. Global Heat Map & Learning to Rank Marco Catalano Lead Engineer - Here Technologies Data Driven Innovation – Rome 2018
  2. 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
  3. 3. Presentation title | Month 00, 20163 © 2016 HERE | HERE Internal Use Only 01 Reverse Geocoder and Global Heat Map
  4. 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. 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
  6. 6. Global Heat Map & Learning to Rank | Data Driven Innovation – Rome 20186 © 2018 HERE Technologies 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. 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. 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.
  9. 9. Global Heat Map & Learning to Rank | Data Driven Innovation – Rome 20189 © 2018 HERE Technologies Visualize & Compare
  10. 10. Global Heat Map & Learning to Rank | Data Driven Innovation – Rome 201810 © 2018 HERE Technologies Visualize & Compare San Francisco 2016 2014
  11. 11. Global Heat Map & Learning to Rank | Data Driven Innovation – Rome 201811 © 2018 HERE Technologies Visualize & Compare Frankfurt am Main 2016 2014
  12. 12. Global Heat Map & Learning to Rank | Data Driven Innovation – Rome 201812 © 2018 HERE Technologies Visualize & Compare Berlin 2016 2014
  13. 13. Presentation title | Month 00, 201613 © 2016 HERE | HERE Internal Use Only 02 Autocompletion and Learning to Rank
  14. 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. 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. 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. 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. 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. 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

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