Y! Time Traveler : Dissection Deepak Shevani Yahoo! Travel

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What is Yahoo! Time Traveler ?

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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 ?

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1 2 3 Select your city Set start & end location Itinerary is ready

Lets see Time Traveler in action !

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How to built this ?

Data requirements

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›  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

Data requirements

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›  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

Design and Architecture

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Itinerary Generation is done in two phases

From PHASE 1

Phase 1 : Computing time spent at POI

Yahoo! Maps

Flickr Data Yahoo! Travel

User Streams POI Data

Generate POI Graph for city

Phase 2 : Generate path between POIs

Start Location End Location Time Constraint

Compute most profitable Path

POI Graph

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

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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 )

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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

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