Search, APIs, capability management and Sensis's journey

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Sensis developed a search capability to drive presence in local search and open up its large business listing database. It selected an open source platform and built a total search solution with relevancy testing, quality management, and continuous deployment. Over time, Sensis evolved from a black box solution to a more transparent and manageable platform through problem identification, gold standard testing, and continuous tuning. Future plans include expanding the query engine and implementing machine learning.

Tecnologia Turismo

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 Technology objectives
• Free to use, we are after the • Develop a total search platform
reporting • Relevancy testing as part of the
development lifecycle
• A framework to identify problem
spaces
• Manageable platform
• Continuous deployments

Project background

• Support for the search
capability team

• Structured vs non
structured data

• Deterministic vs black
box

• Non propriety code base

• Community backing

Platform selection

• A/B testing
• Machine learning
Optimized Lvl 5 • External collaboration
• Multiple contexts

• Online dashboards
• Test environments
Managed Lvl 4 • Dynamic search refinements
• Targets and metrics

• Defined team
• Regular monitoring
Monitored Lvl 3 • Static autosuggest
• Basic linguistics

• Adhoc processes
• Part time team
Adhoc Lvl 2 • Static dictionaries
• Individual led innovation

• No resources
• No reporting
Unmanaged Lvl 1 • Out of the box
features

The Sensis Search capability maturity model
*Courtesy of Pete Crawford & Craig Lonsdale

Location

Intent Chronology
• Name
• Type
Social Graph
• Product
• Spatial

Device

Individual

Context is key

Business Geo Service
Data

Solr Mashery
Business Name Query
Data Search
MongoDB Handler Service
Index API Publisher
Reporting
Type Query
Service
Handler

Historical
search
Data

Reporting
Events

Ontologies

Our architecture

• Moved from a black box Yesterday Today Tomorrow
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

Specific gold sets for each
Path Analysis problem space:
used to identify  Intent
 Spelling & stemming
problems  Location
spaces  Phrase parsing

Features signed off
“Gold Sets” only when they make
used to define a positive impact to
overall quality quality score
score (TREC)

Problem spaces, quality management & tuning

• Data redundancy and
homogeneity
• Solr ranking of rare terms
• Intent differentiation
• Contextual synonyms

Hurdles along the way

• Query engine
• Facets / autosuggest
• Real time tuning
• Machine learning
• Multi term queries
• Scoring thresholds
• Content Value

Where next?

Email: craig.rees@sensis.com.au
www: developers.sensis.com.au
Twitter: @SensisAPI
@ablebagel

Questions?

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Search, APIs, capability management and Sensis's journey

1. Search, APIs, Capability Management and the Sensis Journey Craig Rees

2. • Project background • Platform selection • Search capability • Relevance • Architecture • Quality management • Hurdles • What’s next Today’s menu

3. • 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

4. 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 Technology objectives • Free to use, we are after the • Develop a total search platform reporting • Relevancy testing as part of the development lifecycle • A framework to identify problem spaces • Manageable platform • Continuous deployments Project background

5. Developer portal

6. • Support for the search capability team • Structured vs non structured data • Deterministic vs black box • Non propriety code base • Community backing Platform selection

7. • A/B testing • Machine learning Optimized Lvl 5 • External collaboration • Multiple contexts • Online dashboards • Test environments Managed Lvl 4 • Dynamic search refinements • Targets and metrics • Defined team • Regular monitoring Monitored Lvl 3 • Static autosuggest • Basic linguistics • Adhoc processes • Part time team Adhoc Lvl 2 • Static dictionaries • Individual led innovation • No resources • No reporting Unmanaged Lvl 1 • Out of the box features The Sensis Search capability maturity model *Courtesy of Pete Crawford & Craig Lonsdale

8. Location Intent Chronology • Name • Type Social Graph • Product • Spatial Device Individual Context is key

9. Business Geo Service Data Solr Mashery Business Name Query Data Search MongoDB Handler Service Index API Publisher Reporting Type Query Service Handler Historical search Data Reporting Events Ontologies Our architecture

10. Business Geo Service Data Solr Mashery Business Name Query Data Search MongoDB Handler Service Index API Publisher Reporting Type Query Service Handler Historical search Data Reporting Events Ontologies Data staging

11. Business Geo Service Data Solr Mashery Business Name Query Data Search MongoDB Handler Service Index API Publisher Reporting Type Query Service Handler Historical search Data Reporting Events Ontologies Search

12. Business Geo Service Data Solr Mashery Business Name Query Data Search MongoDB Handler Service Index API Publisher Reporting Type Query Service Handler Historical search Data Reporting Events Ontologies API

13. Business Geo Service Data Solr Mashery Business Name Query Data Search MongoDB Handler Service Index API Publisher Reporting Type Query Service Handler Historical search Data Reporting Events Ontologies API proxy

14. • Moved from a black box Yesterday Today Tomorrow 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

15. Specific gold sets for each Path Analysis problem space: used to identify  Intent  Spelling & stemming problems  Location spaces  Phrase parsing Features signed off “Gold Sets” only when they make used to define a positive impact to overall quality quality score score (TREC) Problem spaces, quality management & tuning

16. Search quality analysis and testing

17. Results examiner

18. Score analysis

19. Tuning

20. Lather, rinse, repeat

21. • Data redundancy and homogeneity • Solr ranking of rare terms • Intent differentiation • Contextual synonyms Hurdles along the way

22. • Query engine • Facets / autosuggest • Real time tuning • Machine learning • Multi term queries • Scoring thresholds • Content Value Where next?

23. Email: craig.rees@sensis.com.au www: developers.sensis.com.au Twitter: @SensisAPI @ablebagel Questions?