"eCommerce Search with Apache Solr", Grant Ingersoll

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eCommerce Search with Apache Solr
Grant Ingersoll
CTO, LucidWorks
Twitter: @gsingers

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Tales from the trenches
• The case of the missing data
• The power of suggestion

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Topics
• Solr powered commerce
– Companies
– Features
• Relevance, relevance, relevance
• Demo

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Solr Powers Leading eCommerce and
Consumer Sites

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Basic Features for eCommerce
• High quality OOTB relevance
• Facets
– Range, Term/Category, Hierarchical, Pivot
• Highlighting
• Did you mean?
• Boosting/Blocking/Landing Pages
• Easy scale

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Advanced Features
• Spatial
– Local
– Route finding
– Open Hours, etc.
• Function Queries
– Inventory, Margin
• Stats Component
– Missing data
– Bounds, etc.

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Tips and Tricks

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Look Before You Leap
• Before undertaking any relevance tuning, you need
to define what “better search” means to you
• Once determined, many ways to test/measure
• Once tested, many ways to fix
http://www.betternetworker.com/files/useruploads/16675/leap.jpg

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Understand your…
• Domain
– Types of documents
– Languages present
– Document structures,
metadata and other features
– Lexical resources: jargon,
synonyms, abbreviations...
– Relationships between
documents
• Users
– Sophistication/Expertise
– Search and Discovery needs
– Known Item vs. Keyword
• Tolerance for Pain
– Managers
– Business Interests
– Release cycles
– Obsession in finding the one
true relevance model (hint, it
doesn’t exist)
– “explain() blindness”

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Known Item vs. Keyword
 eCommerce search often has a split between known item and
keyword search
 You probably have more “wiggle” room for relevancy on
keyword search
 E.g. What should be the top result for a search on “women’s shoes”?
 Known Item should have best matches at the top
 More in a moment

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Debugging
• Check the analysis (more in the next slide)
• Check for data quality issues
• Check your query constructs (slop, boosts, etc.)
• Try alternate query representations
• (exact match)^100 OR (sloppy phrase match)^50 OR (OR query)
• Use Lucene’s explain() or Solr’s &debugQuery

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Signal Processing for Search and Discovery
• Signals power modern relevance
– Clicks, conversions, sharing, history, signatures
• LucidWorks 5 makes it easy to capture and
leverage signals
– Recommendations, analytics, discovery
• Simplifies your data workflow
• Simplify your operational footprint

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Solr Powered Signal Processing
• Use Case: eCommerce
• Data:
– Product catalog (~1.2m items)
– Click data (~3.9M clicks)

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Metadata
• http://www.lucidworks.com
• grant@lucidworks.com
• @gsingers
• Lucene/Solr Revolution
– Washington DC, Nov 11-14
– http://www.lucenerevolution.org