rees craig - search, apis, solr and the sensis journey
DESCRIPTION
Search, APIs, Capability Management and the Sensis Journey Craig Rees • Search capability • Platform selection • Relevance • Architecture • Hurdles • What’s next • Two of the top 10 visited online sites in Australia (WhitePages.com.au and YellowPages.com.au) • Sensis helps Australians find, buy and sell • From print directories to a cross-platform lead generator • Sensis publishes over 1.8 Million business listingsTRANSCRIPT
Search, APIs, Capability Management
and the Sensis Journey
Craig Rees
• Project background
• Platform selection
• Search capability
• Relevance
• Architecture
• Quality management
• Hurdles
• What’s next
Today’s menu
• Sensis helps Australians find, buy and sell
• From print directories to a cross-platform lead generator
• Sensis publishes over 1.8 Million business listings
• Two of the top 10 visited online sites in Australia (WhitePages.com.au and YellowPages.com.au)
Sensis
Business objectives • Drive presence in the local
search market place • Open up the largest database of
business listings in Australia • Reduce the effort required from
local search developers • Free to use, we are after the
reporting
Technology objectives • Develop a total search platform • Relevancy testing as part of the
development lifecycle • A framework to identify problem
spaces • Manageable platform • Continuous deployments
Project background
Developer portal
Platform selection
• Support for the search capability team
• Structured vs non structured data
• Deterministic vs black box
• Non propriety code base
• Community backing
Unmanaged
Adhoc
Monitored
Managed
Optimized
• No resources • No reporting • Out of the box
features
• Adhoc processes • Part time team • Static dictionaries • Individual led innovation
• Defined team • Regular monitoring • Static autosuggest • Basic linguistics
• Online dashboards • Test environments • Dynamic search refinements • Targets and metrics
• A/B testing • Machine learning • External collaboration • Multiple contexts
The Sensis Search capability maturity model *Courtesy of Pete Crawford & Craig Lonsdale
Lvl 5
Lvl 4
Lvl 3
Lvl 2
Lvl 1
Context is key
Intent • Name • Type • Product • Spatial
Location
Chronology
Social Graph
Individual
Device
Historical search Data
MongoDB Business
Data
Geo Service
Index
Name Query Handler
Type Query Handler
Business Data
Search Service
Reporting Service
Reporting Events
Publisher
Solr
API
Ontologies
Mashery
Our architecture
Historical search Data
MongoDB Business
Data
Geo Service
Index
Name Query Handler
Type Query Handler
Business Data
Search Service
Reporting Service
Reporting Events
Publisher
Solr
API
Ontologies
Mashery
Data staging
Historical search Data
MongoDB Business
Data
Geo Service
Index
Name Query Handler
Type Query Handler
Business Data
Search Service
Reporting Service
Reporting Events
Publisher
Solr
API
Ontologies
Mashery
Search
Historical search Data
MongoDB Business
Data
Geo Service
Index
Name Query Handler
Type Query Handler
Business Data
Search Service
Reporting Service
Reporting Events
Publisher
Solr
API
Ontologies
Mashery
API
Historical search Data
MongoDB Business
Data
Geo Service
Index
Name Query Handler
Type Query Handler
Business Data
Search Service
Reporting Service
Reporting Events
Publisher
Solr
API
Ontologies
Mashery
API proxy
• Moved from a black box solution to a manageable platform
• Deliver search improvements without major code changes
• Understand how results were calculated
• Identity problems scientifically
• Continuously tune and test relevance
Evolution of search management
Yesterday Today Tomorrow
Problem spaces, quality management & tuning
Path Analysis used to identify problems spaces
Problem spaces, quality management & tuning
“Gold Sets” used to define overall quality score (TREC)
Features signed off only when they make a positive impact to quality score
Specific gold sets for each problem space:
Ø Intent Ø Spelling & stemming Ø Location Ø Phrase parsing
Search quality analysis and testing
Results examiner
Score analysis
Tuning
Lather, rinse, repeat
Hurdles along the way
• Data redundancy and homogeneity • Solr ranking of rare terms • Intent differentiation • Contextual synonyms
Where next?
• Query engine • Facets / autosuggest • Real time tuning • Machine learning • Multi term queries • Scoring thresholds • Content Value
