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Search, APIs,Capability Managementand
the Sensis Journey
Craig Rees
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Project background Platform selection Search capability Relevance
Architecture Quality management Hurdles Whats next
Todays menu
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Sensis helps Australians find,buy and sellFrom print directories to across-platform lead generator
Sensis publishes over 1.8Million business listings
Two of the top 10 visited onlinesites in Australia
(WhitePages.com.au andYellowPages.com.au)
Sensis
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Business objectives
Drive presence in the localsearch market place
Open up the largest database ofbusiness listings in Australia
Reduce the effort required fromlocal search developers
Free to use, we are after thereporting
Technology objectives
Develop a total search platform
Relevancy testing as part of thedevelopment lifecycle
A framework to identify problemspaces
Manageable platform Continuous deployments
Project background
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Developer portal
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Platform selection
Support for the searchcapability team
Structured vs nonstructured data
Deterministic vs blackbox
Non propriety code base Community backing
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Unmanaged
Adhoc
Monitored
Managed
Optimized
No resourcesNo reportingOut of the box
features
Adhoc processesPart time teamStatic dictionariesIndividual led innovation
Defined teamRegular monitoringStatic autosuggestBasic linguistics
Online dashboardsTest environmentsDynamic search refinementsTargets and metrics
A/B testingMachine learningExternal collaborationMultiple contexts
The Sensis Search capability maturity model*Courtesy of Pete Crawford & Craig Lonsdale
Lvl 5
Lvl 4
Lvl 3
Lvl 2
Lvl 1
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Context is key
Intent
NameType Product Spatial
Location
Chronology
Social Graph
Individual
Device
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HistoricalsearchData
MongoDB
BusinessData
Geo Service
Index
Name QueryHandler
Type QueryHandler
BusinessData
SearchService
ReportingService
ReportingEvents
Publisher
Solr
API
Ontologies
Mashery
Our architecture
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HistoricalsearchData
MongoDB
BusinessData
Geo Service
Index
Name QueryHandler
Type QueryHandler
BusinessData
SearchService
ReportingService
ReportingEvents
Publisher
Solr
API
Ontologies
Mashery
Data staging
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HistoricalsearchData
MongoDB
BusinessData
Geo Service
Index
Name QueryHandler
Type QueryHandler
BusinessData
SearchService
ReportingService
ReportingEvents
Publisher
Solr
API
Ontologies
Mashery
Search
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HistoricalsearchData
MongoDB
BusinessData
Geo Service
Index
Name QueryHandler
Type QueryHandler
BusinessData
SearchService
ReportingService
ReportingEvents
Publisher
Solr
API
Ontologies
Mashery
API
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HistoricalsearchData
MongoDB
BusinessData
Geo Service
Index
Name QueryHandler
Type QueryHandler
BusinessData
SearchService
ReportingService
ReportingEvents
Publisher
Solr
API
Ontologies
Mashery
API proxy
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Moved from a black box solutionto a manageable platform
Deliver search improvementswithout major code changes
Understand how results werecalculated
Identity problems scientifically Continuously tune and test
relevance
Evolution of search management
Yesterday Today Tomorrow
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Problem spaces, quality management & tuning
Path Analysis
used to identify
problemsspaces
Problem spaces, quality management & tuning
Gold Setsused to define
overall quality
score (TREC)
Features signed offonly when they make
a positive impact to
quality score
Specific gold sets for eachproblem space: Intent Spelling & stemming Location Phrase parsing
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Search quality analysis and testing
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Results examiner
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Score analysis
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Tuning
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Lather, rinse, repeat
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Hurdles along the way
Data redundancy and homogeneity
Solr ranking of rare terms Intent differentiation Contextual synonyms
Q i
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Where next?
Query engine Facets / autosuggest Real time tuning Machine learning Multi term queries Scoring thresholds
Content Value
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Questions?
Email: [email protected]: developers.sensis.com.au
Twitter: @SensisAPI
@ablebagel
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