Yahoo! Time Traveler

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Yahoo! Time Traveler

  1. 1. Y! Time Traveler : Dissection Deepak Shevani Yahoo! Travel
  2. 2. 2Confidential What is Yahoo! Time Traveler ?
  3. 3. 3Confidential iOS mobile application Your travel concierge Creates travel itineraries Version 1 launched for 29 cities – Featured 5 times in US AppStore Version 2 launched for 70 cities – Faster, Smarter and Personal !! What is Yahoo! Time Traveler ?
  4. 4. 4Confidential 1 2 3Select your city Set start & end location Itinerary is ready Lets see Time Traveler in action !
  5. 5. 5Confidential How to built this ?
  6. 6. Data requirements 6Confidential ›  Points of interests within a city (POI) •  Latitude, Longitude, Address, Opening Hours, Name, Category ›  Time spent at POI •  Average time that must be spent at this location ›  Distances between POIs •  Driving and Walking distances between locations ›  Algorithm to compute the itinerary
  7. 7. Data requirements 7Confidential ›  Points of interests within a city (POI) •  Latitude, Longitude, Address, Opening Hours, Name, Category •  Use Yahoo! Travel APIs to gather information about POIs ›  Time spent at POI •  Average time that must be spent at this location •  Use Flickr photos to determine average time spent at POIs ›  Distances between POIs •  Driving and Walking distances between locations •  Use Yahoo! Geo APIs to compute these distances ›  Algorithm to compute the itinerary
  8. 8. Design and Architecture 8Confidential Itinerary Generation is done in two phases From PHASE 1 Phase 1 : Computing time spent at POI Yahoo! Maps Flickr DataYahoo! Travel User StreamsPOI Data Generate POI Graph for city Phase 2 : Generate path between POIs Start Location End Location Time Constraint Compute most profitable Path POI Graph
  9. 9. Phase 1 – Flickr Data Mining §  Steps to compute time spent at POIs within a city ›  Extract all geo-tagged Flickr images for a given POI ›  Process the images ordered by click-time and author ›  Deduce the time spent by the users at POIs using first & last timestamps ›  Compute the mean of time spent by various users at a POI §  Use Yahoo! Geo APIs travel time between POIs §  Output : Weighted POI Graph for city 9Confidential
  10. 10. Phase 2 – Path Computation §  Orienteering Problem ›  Given an edge weighted graph G=(V,E,w), and a pair of nodes ‘s’ & ‘t’ - find s-t walk of length at most ‘B’ and that maximizes some function ‘f’ on set of nodes in the path •  Here ‘V’ is vertex set, ‘E’ is Edge set, ‘w’ is weight function, ‘B’ is path budget, ‘f’ is reward function §  Reducing our problem to Orienteering Problem •  Each node in city graph is a POI, with cost = time spent, and price = popularity •  Each edge in city graph has weight = travel time between POIs •  ‘B’ denotes the maximum number of POIs allowed in a path •  Reward Function ‘f’ is proportional to Flickr Users for a POI & its popularity §  Results •  The algorithm computes path between POIs at run time, in less than 2-3 seconds ( |V| < 30 ) 10Confidential
  11. 11. References §  Chandra Chekuri, Martin Pal. A Recursive Greedy Algorithm foe Walks in Directed Graphs, IEEE Symposium 2005 §  Munmun De Chaudhary. DeConstructing Travel Itineraries from tagged Geo Temporal Breadcrumbs WWW2010 §  Yahoo Geo Technologies http://developer.yahoo.com/geo/geoplanet/ §  Flickr APIs http://www.flickr.com/services/api/ §  Yahoo! Travel http://travel.yahoo.com 11Confidential

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