It’s Just Search

Search Camp United Nations, NYC

July 10, 2016

Erik Hatcher

it’s just search

it’s just +search

search?q=

search

Think inside the box. Inside the search box, that is.

The "best"* search results incorporate many more factors than (just) textual matching and relevancy. Search experience owners manage query context rules, signals automatically feed back machine learned factors, users implicit and explicit behaviors filter and weight future interactions. Synergy emerges with several cooperating (just) searches.

This talk will showcase and detail several (just) search examples including rules, typeahead/suggest, signals, and location awareness, bringing them all together into a cohesive search experience.

Abstract

• Lucene/Solr Committer • Apache Software Foundation Member • Creator of Blacklight • Co-author of “Ant in Action” and “Lucene in Action” • Co-founder and Senior Solutions Architect at Lucidworks

About me

@erikhatcher

anatomy of search results

Anatomy of Search Results

Anatomy of Search Results

Relevancy ranked results

Anatomy of Search Results

Facets

Anatomy of Search Results

Highlighting

Grouping

• relevancy ranked results

• faceting

• highlighting

• grouping (collapsing)

• more-like-this

• did-you-mean (spellcheck)

Search Features

typeahead, autocomplete,

suggest, instant search

spell checking

• query a “typed” n-grammed index, and get “typed” results

• type examples: brands, categories, specialties, treatments, providers/doctors, products

• suggest Things Not Strings

• things have attributes: metadata

(known) entity detection

• /select?q=sea…

• q=<what user typed>

• Response includes grouped and ranked results, highlighting, and spellcheck suggestions

typeahead request

signals

• clicks and queries

• add-to-carts

• user history, behavior, and preferences

• geo-location, device

signals and recommendations

signals workflow

• <collection>/select?q=search

• <collection>_signals_aggr/select

• ?q=${q}

• qf, pf, sort

• ==> bq: "id:7^1.0"

signals sub-query

rules

• trigger off query context

• query, filters, date/time, tags

• actions -

• boost, block, banner, redirect, set parameters, rewrite query, etc

• superset of signals

• signals: automated feedback

• rules: manually created

rules

• <collection>/select?q=solr search[&tags=mobile,production]

• <collection>_rules/select

• ?q=${q}

• &fq=-enabled:false

• &fq=…effective_range:$now…

• &fq=…tags:($tags)…

• process rules returned

• manipulate request, append to response

rules sub-query

geo-lookup

• /providers/select?q=jones&zipcode=10301

• /zipcodes/select?q={!term f=zipcode_s v=$zipcode}

zipcodes collectionzipcodes.csv ZipCode,State,City,County,Latitude,Longitude,AreaCode,TimeZone,DST 85701,AZ,Tucson,Pima,32.216091,-110.97099,520,-7,N 10301,NY,Staten Island,Richmond,40.6226,-74.0992,718,-5,Y 22901,VA,Charlottesville,Albemarle,38.071751,-78.503937,434,-5,Y,

fl += dist:geodist() fq += {!geofilt} pt = ${zipcode_doc.latlon_srpt} sfield = locations_rpt d = 10 sort = geodist() asc

conclusion

•simple things working together

•whole is greater than the sum of the parts

• feedback loops •emergent behavior

Synergy

• Presentation materials:

• https://github.com/erikhatcher/searchcamp

• Search Hub:

• http://searchhub.lucidworks.com/

• Fusion (platform used for presentation):

• https://lucidworks.com/products/fusion/

See Also

@erikhatcher

Lucidworks Fusion Is Search-Driven Everything

Access data from anywhere to build intelligent, data-driven applications.

Data Acquisition

Indexing & Streaming

Smart Access API

Recommendations & Alerts Analytics & InsightsExtreme Relevancy

CUSTOMER SERVICE

RESEARCH PORTAL

DIGITAL CONTENT

CUSTOMER INSIGHTS

FRAUD SURVEILLANCE

ONLINERETAIL

2016

OCTOBER 11-14BOSTON, MAhttp://lucenerevolution.org/

• https://commons.wikimedia.org/wiki/File:Ant_bridge.jpg

• http://jillian.rootaction.net/?p=340

• https://commons.wikimedia.org/wiki/File:Turtles.on.a.stone.in.brazil.jpg

image credits