SlideShare ist ein Scribd-Unternehmen logo
Modern	
  Techniques	
  for	
  Better	
  Search	
  
Relevance	
  in	
  Fusion
Grant	
  Ingersoll	
  
CTO,	
  Lucidworks	
  
November	
  15,	
  2017
😊
iPad case
😊
iPad case
!
"ipad
accessory"~3
OR "ipad
case"~5
1.
15.
Webinar: Modern Techniques for Better Search Relevance with Fusion
👎
Webinar: Modern Techniques for Better Search Relevance with Fusion
So,	
  what	
  do	
  you	
  do?
RT(F)M!
if	
  (doc.name.contains(“Vikings”)){	
  
doc.boost	
  =	
  100	
  
}
OR
q:(MAIN	
  QUERY)	
  OR	
  (name:Vikings)^10
Index	
  Time:
Query	
  Time:
Webinar: Modern Techniques for Better Search Relevance with Fusion
Webinar: Modern Techniques for Better Search Relevance with Fusion
• Term	
  Frequency:	
  “How	
  well	
  a	
  term	
  describes	
  a	
  document”	
  
• Measure:	
  how	
  often	
  a	
  term	
  occurs	
  per	
  document	
  
• Inverse	
  Document	
  Frequency:	
  “How	
  important	
  is	
  a	
  term	
  
overall”	
  
• Measure:	
  how	
  rare	
  the	
  term	
  is	
  across	
  all	
  documents
TF*IDF
Score(q,	
  d)	
  =	
  	
  	
  
	
  	
  	
  	
  	
  	
  ∑	
  	
  idf(t)	
  ·∙	
  (	
  tf(t	
  in	
  d)	
  ·∙	
  (k	
  +	
  1)	
  )	
  /	
  (	
  tf(t	
  in	
  d)	
  +	
  k	
  ·∙	
  (1	
  –	
  b	
  +	
  b	
  ·∙	
  |d|	
  /	
  avgdl	
  )	
  
	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  t	
  in	
  q	
  
Where:	
  	
  
	
  	
  	
  	
  	
  	
  	
  	
  	
  t	
  =	
  term;	
  d	
  =	
  document;	
  q	
  =	
  query;	
  i	
  =	
  index

	
  	
  	
  	
  	
  	
  	
  	
  	
  tf(t	
  in	
  d)	
  	
  =	
  	
  numTermOccurrencesInDocument	
  ½	
  
	
  	
  	
  	
  	
  	
  	
  	
  	
  idf(t)	
  =	
  	
  1	
  +	
  log	
  (numDocs	
  /	
  (docFreq	
  +	
  1))	
  
	
  	
  	
  	
  	
  	
  	
  	
  	
  |d|	
  =	
  	
  ∑	
  1	
  
	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  t	
  in	
  d	
  
	
  	
  	
  	
  	
  	
  	
  	
  	
  avgdl	
  =	
  =	
  (	
  ∑	
  |d|	
  	
  )	
  /	
  (	
  ∑	
  1	
  )	
  )	
  
	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  d	
  in	
  i	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  d	
  in	
  i	
  
	
  	
  	
  	
  	
  	
  	
  	
  	
  k	
  =	
  Free	
  parameter.	
  Usually	
  ~1.2	
  to	
  2.0.	
  Increases	
  term	
  frequency	
  saturation	
  point.	
  
	
  	
  	
  	
  	
  	
  	
  	
  	
  b	
  =	
  Free	
  parameter.	
  Usually	
  ~0.75.	
  Increases	
  impact	
  of	
  document	
  normalization.
BM25	
  (aka	
  Okapi)
• Capture	
  and	
  log	
  pretty	
  much	
  everything	
  
• Searches,	
  clicks,	
  time	
  on	
  page,	
  seen/not,	
  etc.	
  
• Precision	
  —	
  Of	
  those	
  shown,	
  what’s	
  relevant?	
  
• Recall	
  —	
  Of	
  all	
  that’s	
  relevant,	
  what	
  was	
  found?	
  
• NDCG	
  —	
  Account	
  for	
  position
Measure,	
  Measure,	
  Measure
Lather,	
  Rinse,	
  Repeat
Webinar: Modern Techniques for Better Search Relevance with Fusion
💡
WWGD?
👮 ♀👜 👲 💼 !♀😎 🎓 🎅 "👸 🚴 💁 ♂🚜 !🙎 ♂👮 ♀👜 👲 💼 !♀😎 🎓 🎅 "👸 🚴 💁 ♂🚜 !🙎 ♂🚵 ♀!💆 0
💃 🔬 !♀👵 🏃 ♀💂 👔 8 🚶 🚵 ♀!💆 0 👮 1 👮 ♀👜 👲 💼 !♀😎 🎓 🎅 "👸 🚴 💁 ♂🚜 !🙎 ♂🚵 ♀!💆 0
"🏇 "♀🏈 "💆 ♂🏄 ♀💇 👷 !👷 ♀🏂 💇 ♂👴 👼 💃 🔬 !♀👵 🏃 ♀💂 👔 8 🚶 🚵 ♀!💆 0 👮 1 🚵 ♀!💆 0
!🎪 🚶 ♀👮 ♀👜 👲 💼 !♀😎 🎓 🎅 "👸 🚴 💁 ♂🚜 !🙎 ♂💃 🔬 !♀👵 🏃 ♀💂 👔 8 🚶 🚵 ♀!💆 0 👮 🚵 ♀!
💃 🔬 !♀👵 🏃 ♀💂 👔 8 🚶 🚵 ♀!💆 0 👮 1 💃 🔬 !♀👵 🏃 ♀💂 👔 8 🚶 🚵 ♀!💆 0 👮 1 🚵 ♀!💆 0
🏇 "♀🏈 "💆 ♂🏄 ♀💇 👷 !👷 ♀🏂 💇 ♂👴 👼 💂 ♀B 🎒 !🎪 🚶 ♀💃 🔬 !♀👵 🏃 ♀💂 👔 8 🚶 🚵 ♀!💆 0
Magic
Guessing
Core	
  Information	
  Theory	
  (aka	
  Lucene/Solr)
Search	
  Aids	
  (Facets,	
  Did	
  You	
  Mean,	
  Highlighting)
Machine	
  Learning/NLP	
  	
  
(Clicks,	
  Crowd	
  Sourcing,	
  Recs,	
  Personalization,	
  User	
  feedback)
Rules,	
  Domain	
  Specific	
  Knowledge
fuhgeddaboudit
Content Collabora*on Context
Core	
  Solr	
  capabili*es:	
  text	
  matching,	
  face*ng,	
  
spell	
  checking,	
  highligh*ng	
  
Business	
  Rules	
  for	
  content:	
  landing	
  pages,	
  boost/
block,	
  promopons,	
  etc.
Leverage	
  collec*ve	
  intelligence	
  to	
  predict	
  what	
  
users	
  will	
  do	
  based	
  on	
  historical,	
  aggregated	
  
data	
  
Recommenders,	
  Popularity,	
  Search	
  Paths
Who	
  are	
  you?	
  	
  Where	
  are	
  you?	
  	
  What	
  have	
  you	
  
done	
  previously?	
  
User/Market	
  Segmentapon,	
  Roles,	
  Security,	
  
Personalizapon
Next Generation Relevance
But	
  What	
  About	
  the	
  Real	
  World?	
  Indexing	
  Edipon
Machine	
  Learning/
NLP	
  
NER,	
  Topic	
  Detection,	
  
Clustering	
  Word2Vec,	
  etc.
Domain	
  Rules:	
  
Synonyms,	
  Regexes,	
  
Lexical	
  Resources
Extraction
Load	
  Into	
  Spark
Build	
  W2V,	
  
PageRank,	
  Topic,	
  
Clustering	
  Models
Offline
Content
Models
Real	
  World?	
  Query	
  Edipon
Query	
  Intent	
  	
  
Strategic,	
  Tactical,	
  
Semantic😊
iPad case
Head/Tail/
Clickstream/
Recommenders
User	
  Factors:	
  
Segmentation,	
  Location,	
  
History,	
  Profile,	
  Security
Parse
Domain	
  Specific	
  
Rules
Transform	
  Results
…
Cascading	
  Rerankers	
  
Learn	
  To	
  Rank	
  (multi-­‐
model),	
  Bias	
  corrections
Real	
  World?	
  Users	
  Edipon
Load	
  Into	
  SparkSignals Query	
  Analysis
Recommenders/
Personalization
😊
iPad case
Query	
  Edition
Raw
Models
Clickstream	
  Models
(Exact/Original	
  Match)^X	
  	
  
(Sloppy	
  Phrase)~M^Y	
  	
  
(AND	
  Q)^Z	
  	
  
(OR	
  Q)^XX	
  
(Expansions/Click/Head/Tail	
  Boosts)^YY	
  
(Personalization	
  Biases)^ZZ	
  
({!ltr	
  model=…})	
  
Filters+Options:	
  security,	
  rules,	
  hard	
  preferences,	
  categories
The	
  Perfect(?!?)	
  Query*	
  	
  YMMV!
}	
  Precision
Recall
Caveat	
  Emptor!
*	
  Note:	
  there	
  are	
  a	
  lot	
  of	
  variations	
  on	
  this.	
  	
  edismax	
  handles	
  most
Learn	
  to	
  Rank
X	
  >	
  Y	
  >	
  Z	
  >	
  XX	
  
All	
  weights	
  can	
  be	
  learned
• Don’t	
  take	
  my	
  word	
  for	
  it,	
  experiment!	
  
• A/B	
  Tests,	
  Multi-­‐arm	
  Bandits	
  
• Good	
  primer:	
  	
  
• http://www.slideshare.net/InfoQ/online-­‐controlled-­‐experiments-­‐introduction-­‐
insights-­‐scaling-­‐and-­‐humbling-­‐statistics	
  
• Rules	
  are	
  fine,	
  as	
  long	
  as	
  the	
  are	
  contained,	
  have	
  a	
  lifespan	
  
and	
  are	
  measured	
  for	
  effectiveness
Experimentapon,	
  Not	
  Editorializapon
Show	
  Us	
  Already,	
  Will	
  You!
29
Lucidworks Fusion Product Suite
The Lucidworks platform provides all of the components needed to create and 

run smart enterprise and consumer applications
Create rich UI with modular components for
web and mobile
Surface the insights that matter most with the power
of machine learning and artificial intelligence
Highly scalable search engine and NoSQL datastore
that gives you instant access to all your data
Combine the power of
the Fusion stack with
the simplicity you’d
expect in a SaaS-based
application
Lucidworks Fusion Architecture
Web App Mobile
BI/Analytics
Logs File
Web Database
Box/
Dropbox
Elasticsearch
SDK
Sharepoint
Slack
Jive
Connectors
Admin UI
Search
Analytics
Visualization
REST/
SQL
Hadoop
Google Drive
Security Built In
Proven Speed CDCR
Extensible Scalable Responsive
NLP: NER, Phrases, POS
Query Intent & Doc Classification
Recommenders
Anomaly Detection
Signals and Query Analytics
Clustering
A/B Testing
ETL and Query Pipelines
Alerting and Messaging
SQL and Catalog
Scheduling
Connectors/Federation
Import/Export
Custom Jobs
RulesTopic Detection
Custom
Search
Devs
Data
Scientists
Business
Users
Cross Cutting Features
HDFS (Optional)
Fusion: Meeting the Search Challenge
Relevance and Discovery
Business Support
Intelligence
Open & Scalable
Signal Proc. Machine Learning NLP Math/Stats
Proven Search Extensible Simplified DevOps Real Time
Query/Doc Simulations Rules Analytics User Interface
Personalization Recommendations Query Intent Experimentation (A/B)
Demo Details
• Ecommerce	
  Data	
  Set	
  
-­‐ Product	
  Catalog:	
  ~1.3M	
  
-­‐ Signals:	
  1	
  month	
  of	
  query,	
  document	
  logs	
  
• Fusion	
  3.1	
  +	
  Rules	
  (open	
  source	
  add-­‐on	
  module)	
  +	
  Solr	
  LTR	
  contrib	
  
• Spark	
  2.x,	
  Solr	
  6.5	
  
• App	
  Studio	
  UI	
  (hyp://twigkit.com)
Demo	
  Details
• http://lucidworks.com	
  
• grant@	
  
• http://lucene.apache.org/solr	
  
• http://spark.apache.org/	
  
• https://github.com/lucidworks/spark-­‐solr	
  
• https://cwiki.apache.org/confluence/display/solr/Learning+To+Rank	
  
• Bloomberg	
  talk	
  on	
  LTR	
  https://www.youtube.com/watch?
v=M7BKwJoh96s
Resources

Weitere ähnliche Inhalte

Was ist angesagt?

Click-through relevance ranking in solr &  lucid works enterprise - By Andrz...
 Click-through relevance ranking in solr &  lucid works enterprise - By Andrz... Click-through relevance ranking in solr &  lucid works enterprise - By Andrz...
Click-through relevance ranking in solr &  lucid works enterprise - By Andrz...
lucenerevolution
 
Building a real time big data analytics platform with solr
Building a real time big data analytics platform with solrBuilding a real time big data analytics platform with solr
Building a real time big data analytics platform with solr
Trey Grainger
 
Building a real time, solr-powered recommendation engine
Building a real time, solr-powered recommendation engineBuilding a real time, solr-powered recommendation engine
Building a real time, solr-powered recommendation engine
Trey Grainger
 
Searching and Querying Knowledge Graphs with Solr/SIREn - A Reference Archite...
Searching and Querying Knowledge Graphs with Solr/SIREn - A Reference Archite...Searching and Querying Knowledge Graphs with Solr/SIREn - A Reference Archite...
Searching and Querying Knowledge Graphs with Solr/SIREn - A Reference Archite...
Lucidworks
 
Solr 6.0 Graph Query Overview
Solr 6.0 Graph Query OverviewSolr 6.0 Graph Query Overview
Solr 6.0 Graph Query Overview
Kevin Watters
 
Extending Solr: Building a Cloud-like Knowledge Discovery Platform
Extending Solr: Building a Cloud-like Knowledge Discovery PlatformExtending Solr: Building a Cloud-like Knowledge Discovery Platform
Extending Solr: Building a Cloud-like Knowledge Discovery Platform
Trey Grainger
 
Webinar: Solr 6 Deep Dive - SQL and Graph
Webinar: Solr 6 Deep Dive - SQL and GraphWebinar: Solr 6 Deep Dive - SQL and Graph
Webinar: Solr 6 Deep Dive - SQL and Graph
Lucidworks
 
Solr Graph Query: Presented by Kevin Watters, KMW Technology
Solr Graph Query: Presented by Kevin Watters, KMW TechnologySolr Graph Query: Presented by Kevin Watters, KMW Technology
Solr Graph Query: Presented by Kevin Watters, KMW Technology
Lucidworks
 
Search is the UI
Search is the UI Search is the UI
Search is the UI
danielbeach
 
Leveraging Lucene/Solr as a Knowledge Graph and Intent Engine
Leveraging Lucene/Solr as a Knowledge Graph and Intent EngineLeveraging Lucene/Solr as a Knowledge Graph and Intent Engine
Leveraging Lucene/Solr as a Knowledge Graph and Intent Engine
Trey Grainger
 
Learning to Rank in Solr: Presented by Michael Nilsson & Diego Ceccarelli, Bl...
Learning to Rank in Solr: Presented by Michael Nilsson & Diego Ceccarelli, Bl...Learning to Rank in Solr: Presented by Michael Nilsson & Diego Ceccarelli, Bl...
Learning to Rank in Solr: Presented by Michael Nilsson & Diego Ceccarelli, Bl...
Lucidworks
 
Webinar: Simpler Semantic Search with Solr
Webinar: Simpler Semantic Search with SolrWebinar: Simpler Semantic Search with Solr
Webinar: Simpler Semantic Search with Solr
Lucidworks
 
Cool bonsai cool - an introduction to ElasticSearch
Cool bonsai cool - an introduction to ElasticSearchCool bonsai cool - an introduction to ElasticSearch
Cool bonsai cool - an introduction to ElasticSearch
clintongormley
 
Building Client-side Search Applications with Solr
Building Client-side Search Applications with SolrBuilding Client-side Search Applications with Solr
Building Client-side Search Applications with Solr
lucenerevolution
 
Deduplication Using Solr: Presented by Neeraj Jain, Stubhub
Deduplication Using Solr: Presented by Neeraj Jain, StubhubDeduplication Using Solr: Presented by Neeraj Jain, Stubhub
Deduplication Using Solr: Presented by Neeraj Jain, Stubhub
Lucidworks
 
Использование Elasticsearch для организации поиска по сайту
Использование Elasticsearch для организации поиска по сайтуИспользование Elasticsearch для организации поиска по сайту
Использование Elasticsearch для организации поиска по сайту
Olga Lavrentieva
 
Reflected Intelligence - Lucene/Solr as a self-learning data system: Presente...
Reflected Intelligence - Lucene/Solr as a self-learning data system: Presente...Reflected Intelligence - Lucene/Solr as a self-learning data system: Presente...
Reflected Intelligence - Lucene/Solr as a self-learning data system: Presente...
Lucidworks
 
Exploring Direct Concept Search - Steve Rowe, Lucidworks
Exploring Direct Concept Search - Steve Rowe, LucidworksExploring Direct Concept Search - Steve Rowe, Lucidworks
Exploring Direct Concept Search - Steve Rowe, Lucidworks
Lucidworks
 
Coffee, Danish & Search: Presented by Alan Woodward & Charlie Hull, Flax
Coffee, Danish & Search: Presented by Alan Woodward & Charlie Hull, FlaxCoffee, Danish & Search: Presented by Alan Woodward & Charlie Hull, Flax
Coffee, Danish & Search: Presented by Alan Woodward & Charlie Hull, Flax
Lucidworks
 

Was ist angesagt? (19)

Click-through relevance ranking in solr &  lucid works enterprise - By Andrz...
 Click-through relevance ranking in solr &  lucid works enterprise - By Andrz... Click-through relevance ranking in solr &  lucid works enterprise - By Andrz...
Click-through relevance ranking in solr &  lucid works enterprise - By Andrz...
 
Building a real time big data analytics platform with solr
Building a real time big data analytics platform with solrBuilding a real time big data analytics platform with solr
Building a real time big data analytics platform with solr
 
Building a real time, solr-powered recommendation engine
Building a real time, solr-powered recommendation engineBuilding a real time, solr-powered recommendation engine
Building a real time, solr-powered recommendation engine
 
Searching and Querying Knowledge Graphs with Solr/SIREn - A Reference Archite...
Searching and Querying Knowledge Graphs with Solr/SIREn - A Reference Archite...Searching and Querying Knowledge Graphs with Solr/SIREn - A Reference Archite...
Searching and Querying Knowledge Graphs with Solr/SIREn - A Reference Archite...
 
Solr 6.0 Graph Query Overview
Solr 6.0 Graph Query OverviewSolr 6.0 Graph Query Overview
Solr 6.0 Graph Query Overview
 
Extending Solr: Building a Cloud-like Knowledge Discovery Platform
Extending Solr: Building a Cloud-like Knowledge Discovery PlatformExtending Solr: Building a Cloud-like Knowledge Discovery Platform
Extending Solr: Building a Cloud-like Knowledge Discovery Platform
 
Webinar: Solr 6 Deep Dive - SQL and Graph
Webinar: Solr 6 Deep Dive - SQL and GraphWebinar: Solr 6 Deep Dive - SQL and Graph
Webinar: Solr 6 Deep Dive - SQL and Graph
 
Solr Graph Query: Presented by Kevin Watters, KMW Technology
Solr Graph Query: Presented by Kevin Watters, KMW TechnologySolr Graph Query: Presented by Kevin Watters, KMW Technology
Solr Graph Query: Presented by Kevin Watters, KMW Technology
 
Search is the UI
Search is the UI Search is the UI
Search is the UI
 
Leveraging Lucene/Solr as a Knowledge Graph and Intent Engine
Leveraging Lucene/Solr as a Knowledge Graph and Intent EngineLeveraging Lucene/Solr as a Knowledge Graph and Intent Engine
Leveraging Lucene/Solr as a Knowledge Graph and Intent Engine
 
Learning to Rank in Solr: Presented by Michael Nilsson & Diego Ceccarelli, Bl...
Learning to Rank in Solr: Presented by Michael Nilsson & Diego Ceccarelli, Bl...Learning to Rank in Solr: Presented by Michael Nilsson & Diego Ceccarelli, Bl...
Learning to Rank in Solr: Presented by Michael Nilsson & Diego Ceccarelli, Bl...
 
Webinar: Simpler Semantic Search with Solr
Webinar: Simpler Semantic Search with SolrWebinar: Simpler Semantic Search with Solr
Webinar: Simpler Semantic Search with Solr
 
Cool bonsai cool - an introduction to ElasticSearch
Cool bonsai cool - an introduction to ElasticSearchCool bonsai cool - an introduction to ElasticSearch
Cool bonsai cool - an introduction to ElasticSearch
 
Building Client-side Search Applications with Solr
Building Client-side Search Applications with SolrBuilding Client-side Search Applications with Solr
Building Client-side Search Applications with Solr
 
Deduplication Using Solr: Presented by Neeraj Jain, Stubhub
Deduplication Using Solr: Presented by Neeraj Jain, StubhubDeduplication Using Solr: Presented by Neeraj Jain, Stubhub
Deduplication Using Solr: Presented by Neeraj Jain, Stubhub
 
Использование Elasticsearch для организации поиска по сайту
Использование Elasticsearch для организации поиска по сайтуИспользование Elasticsearch для организации поиска по сайту
Использование Elasticsearch для организации поиска по сайту
 
Reflected Intelligence - Lucene/Solr as a self-learning data system: Presente...
Reflected Intelligence - Lucene/Solr as a self-learning data system: Presente...Reflected Intelligence - Lucene/Solr as a self-learning data system: Presente...
Reflected Intelligence - Lucene/Solr as a self-learning data system: Presente...
 
Exploring Direct Concept Search - Steve Rowe, Lucidworks
Exploring Direct Concept Search - Steve Rowe, LucidworksExploring Direct Concept Search - Steve Rowe, Lucidworks
Exploring Direct Concept Search - Steve Rowe, Lucidworks
 
Coffee, Danish & Search: Presented by Alan Woodward & Charlie Hull, Flax
Coffee, Danish & Search: Presented by Alan Woodward & Charlie Hull, FlaxCoffee, Danish & Search: Presented by Alan Woodward & Charlie Hull, Flax
Coffee, Danish & Search: Presented by Alan Woodward & Charlie Hull, Flax
 

Ähnlich wie Webinar: Modern Techniques for Better Search Relevance with Fusion

SDSC18 and DSATL Meetup March 2018
SDSC18 and DSATL Meetup March 2018 SDSC18 and DSATL Meetup March 2018
SDSC18 and DSATL Meetup March 2018
CareerBuilder.com
 
Introduction to Azure DocumentDB
Introduction to Azure DocumentDBIntroduction to Azure DocumentDB
Introduction to Azure DocumentDB
Denny Lee
 
NATS - A new nervous system for distributed cloud platforms
NATS - A new nervous system for distributed cloud platformsNATS - A new nervous system for distributed cloud platforms
NATS - A new nervous system for distributed cloud platforms
Derek Collison
 
Web technology: Web search
Web technology: Web searchWeb technology: Web search
Web technology: Web search
Victor de Boer
 
Short URLs, Big Fun
Short URLs, Big FunShort URLs, Big Fun
Short URLs, Big Fun
Hilary Mason
 
Getting to Know Your Data with R
Getting to Know Your Data with RGetting to Know Your Data with R
Getting to Know Your Data with R
Stephen Withington
 
Bridging Big Data and Data Science Using Scalable Workflows
Bridging Big Data and Data Science Using Scalable WorkflowsBridging Big Data and Data Science Using Scalable Workflows
Bridging Big Data and Data Science Using Scalable Workflows
Ilkay Altintas, Ph.D.
 
Searching Chinese Patents Presentation at Enterprise Data World
Searching Chinese Patents Presentation at Enterprise Data WorldSearching Chinese Patents Presentation at Enterprise Data World
Searching Chinese Patents Presentation at Enterprise Data World
OpenSource Connections
 
Software Architecture and Predictive Models in R
Software Architecture and Predictive Models in RSoftware Architecture and Predictive Models in R
Software Architecture and Predictive Models in R
Harlan Harris
 
Data Science with Spark
Data Science with SparkData Science with Spark
Data Science with Spark
Krishna Sankar
 
Reflected intelligence evolving self-learning data systems
Reflected intelligence  evolving self-learning data systemsReflected intelligence  evolving self-learning data systems
Reflected intelligence evolving self-learning data systems
Trey Grainger
 
Webinar: Scaling MongoDB
Webinar: Scaling MongoDBWebinar: Scaling MongoDB
Webinar: Scaling MongoDB
MongoDB
 
So You Want to be an OpenStack Contributor
So You Want to be an OpenStack ContributorSo You Want to be an OpenStack Contributor
So You Want to be an OpenStack Contributor
Anne Gentle
 
The Hitchhiker's Guide to Machine Learning with Python & Apache Spark
The Hitchhiker's Guide to Machine Learning with Python & Apache SparkThe Hitchhiker's Guide to Machine Learning with Python & Apache Spark
The Hitchhiker's Guide to Machine Learning with Python & Apache Spark
Krishna Sankar
 
The Lost Tales of Platform Design (February 2017)
The Lost Tales of Platform Design (February 2017)The Lost Tales of Platform Design (February 2017)
The Lost Tales of Platform Design (February 2017)
Julien SIMON
 
Big data visualization frameworks and applications at Kitware
Big data visualization frameworks and applications at KitwareBig data visualization frameworks and applications at Kitware
Big data visualization frameworks and applications at Kitware
bigdataviz_bay
 
Data Modelling at Scale
Data Modelling at ScaleData Modelling at Scale
Data Modelling at Scale
David Simons
 
The Relevance of the Apache Solr Semantic Knowledge Graph
The Relevance of the Apache Solr Semantic Knowledge GraphThe Relevance of the Apache Solr Semantic Knowledge Graph
The Relevance of the Apache Solr Semantic Knowledge Graph
Trey Grainger
 
Reflected Intelligence: Lucene/Solr as a self-learning data system
Reflected Intelligence: Lucene/Solr as a self-learning data systemReflected Intelligence: Lucene/Solr as a self-learning data system
Reflected Intelligence: Lucene/Solr as a self-learning data system
Trey Grainger
 
Building a Lightweight Discovery Interface for Chinese Patents, Presented by ...
Building a Lightweight Discovery Interface for Chinese Patents, Presented by ...Building a Lightweight Discovery Interface for Chinese Patents, Presented by ...
Building a Lightweight Discovery Interface for Chinese Patents, Presented by ...
Lucidworks (Archived)
 

Ähnlich wie Webinar: Modern Techniques for Better Search Relevance with Fusion (20)

SDSC18 and DSATL Meetup March 2018
SDSC18 and DSATL Meetup March 2018 SDSC18 and DSATL Meetup March 2018
SDSC18 and DSATL Meetup March 2018
 
Introduction to Azure DocumentDB
Introduction to Azure DocumentDBIntroduction to Azure DocumentDB
Introduction to Azure DocumentDB
 
NATS - A new nervous system for distributed cloud platforms
NATS - A new nervous system for distributed cloud platformsNATS - A new nervous system for distributed cloud platforms
NATS - A new nervous system for distributed cloud platforms
 
Web technology: Web search
Web technology: Web searchWeb technology: Web search
Web technology: Web search
 
Short URLs, Big Fun
Short URLs, Big FunShort URLs, Big Fun
Short URLs, Big Fun
 
Getting to Know Your Data with R
Getting to Know Your Data with RGetting to Know Your Data with R
Getting to Know Your Data with R
 
Bridging Big Data and Data Science Using Scalable Workflows
Bridging Big Data and Data Science Using Scalable WorkflowsBridging Big Data and Data Science Using Scalable Workflows
Bridging Big Data and Data Science Using Scalable Workflows
 
Searching Chinese Patents Presentation at Enterprise Data World
Searching Chinese Patents Presentation at Enterprise Data WorldSearching Chinese Patents Presentation at Enterprise Data World
Searching Chinese Patents Presentation at Enterprise Data World
 
Software Architecture and Predictive Models in R
Software Architecture and Predictive Models in RSoftware Architecture and Predictive Models in R
Software Architecture and Predictive Models in R
 
Data Science with Spark
Data Science with SparkData Science with Spark
Data Science with Spark
 
Reflected intelligence evolving self-learning data systems
Reflected intelligence  evolving self-learning data systemsReflected intelligence  evolving self-learning data systems
Reflected intelligence evolving self-learning data systems
 
Webinar: Scaling MongoDB
Webinar: Scaling MongoDBWebinar: Scaling MongoDB
Webinar: Scaling MongoDB
 
So You Want to be an OpenStack Contributor
So You Want to be an OpenStack ContributorSo You Want to be an OpenStack Contributor
So You Want to be an OpenStack Contributor
 
The Hitchhiker's Guide to Machine Learning with Python & Apache Spark
The Hitchhiker's Guide to Machine Learning with Python & Apache SparkThe Hitchhiker's Guide to Machine Learning with Python & Apache Spark
The Hitchhiker's Guide to Machine Learning with Python & Apache Spark
 
The Lost Tales of Platform Design (February 2017)
The Lost Tales of Platform Design (February 2017)The Lost Tales of Platform Design (February 2017)
The Lost Tales of Platform Design (February 2017)
 
Big data visualization frameworks and applications at Kitware
Big data visualization frameworks and applications at KitwareBig data visualization frameworks and applications at Kitware
Big data visualization frameworks and applications at Kitware
 
Data Modelling at Scale
Data Modelling at ScaleData Modelling at Scale
Data Modelling at Scale
 
The Relevance of the Apache Solr Semantic Knowledge Graph
The Relevance of the Apache Solr Semantic Knowledge GraphThe Relevance of the Apache Solr Semantic Knowledge Graph
The Relevance of the Apache Solr Semantic Knowledge Graph
 
Reflected Intelligence: Lucene/Solr as a self-learning data system
Reflected Intelligence: Lucene/Solr as a self-learning data systemReflected Intelligence: Lucene/Solr as a self-learning data system
Reflected Intelligence: Lucene/Solr as a self-learning data system
 
Building a Lightweight Discovery Interface for Chinese Patents, Presented by ...
Building a Lightweight Discovery Interface for Chinese Patents, Presented by ...Building a Lightweight Discovery Interface for Chinese Patents, Presented by ...
Building a Lightweight Discovery Interface for Chinese Patents, Presented by ...
 

Mehr von Lucidworks

Search is the Tip of the Spear for Your B2B eCommerce Strategy
Search is the Tip of the Spear for Your B2B eCommerce StrategySearch is the Tip of the Spear for Your B2B eCommerce Strategy
Search is the Tip of the Spear for Your B2B eCommerce Strategy
Lucidworks
 
Drive Agent Effectiveness in Salesforce
Drive Agent Effectiveness in SalesforceDrive Agent Effectiveness in Salesforce
Drive Agent Effectiveness in Salesforce
Lucidworks
 
How Crate & Barrel Connects Shoppers with Relevant Products
How Crate & Barrel Connects Shoppers with Relevant ProductsHow Crate & Barrel Connects Shoppers with Relevant Products
How Crate & Barrel Connects Shoppers with Relevant Products
Lucidworks
 
Lucidworks & IMRG Webinar – Best-In-Class Retail Product Discovery
Lucidworks & IMRG Webinar – Best-In-Class Retail Product DiscoveryLucidworks & IMRG Webinar – Best-In-Class Retail Product Discovery
Lucidworks & IMRG Webinar – Best-In-Class Retail Product Discovery
Lucidworks
 
Connected Experiences Are Personalized Experiences
Connected Experiences Are Personalized ExperiencesConnected Experiences Are Personalized Experiences
Connected Experiences Are Personalized Experiences
Lucidworks
 
Intelligent Insight Driven Policing with MC+A, Toronto Police Service and Luc...
Intelligent Insight Driven Policing with MC+A, Toronto Police Service and Luc...Intelligent Insight Driven Policing with MC+A, Toronto Police Service and Luc...
Intelligent Insight Driven Policing with MC+A, Toronto Police Service and Luc...
Lucidworks
 
[Webinar] Intelligent Policing. Leveraging Data to more effectively Serve Com...
[Webinar] Intelligent Policing. Leveraging Data to more effectively Serve Com...[Webinar] Intelligent Policing. Leveraging Data to more effectively Serve Com...
[Webinar] Intelligent Policing. Leveraging Data to more effectively Serve Com...
Lucidworks
 
Preparing for Peak in Ecommerce | eTail Asia 2020
Preparing for Peak in Ecommerce | eTail Asia 2020Preparing for Peak in Ecommerce | eTail Asia 2020
Preparing for Peak in Ecommerce | eTail Asia 2020
Lucidworks
 
Accelerate The Path To Purchase With Product Discovery at Retail Innovation C...
Accelerate The Path To Purchase With Product Discovery at Retail Innovation C...Accelerate The Path To Purchase With Product Discovery at Retail Innovation C...
Accelerate The Path To Purchase With Product Discovery at Retail Innovation C...
Lucidworks
 
AI-Powered Linguistics and Search with Fusion and Rosette
AI-Powered Linguistics and Search with Fusion and RosetteAI-Powered Linguistics and Search with Fusion and Rosette
AI-Powered Linguistics and Search with Fusion and Rosette
Lucidworks
 
The Service Industry After COVID-19: The Soul of Service in a Virtual Moment
The Service Industry After COVID-19: The Soul of Service in a Virtual MomentThe Service Industry After COVID-19: The Soul of Service in a Virtual Moment
The Service Industry After COVID-19: The Soul of Service in a Virtual Moment
Lucidworks
 
Webinar: Smart answers for employee and customer support after covid 19 - Europe
Webinar: Smart answers for employee and customer support after covid 19 - EuropeWebinar: Smart answers for employee and customer support after covid 19 - Europe
Webinar: Smart answers for employee and customer support after covid 19 - Europe
Lucidworks
 
Smart Answers for Employee and Customer Support After COVID-19
Smart Answers for Employee and Customer Support After COVID-19Smart Answers for Employee and Customer Support After COVID-19
Smart Answers for Employee and Customer Support After COVID-19
Lucidworks
 
Applying AI & Search in Europe - featuring 451 Research
Applying AI & Search in Europe - featuring 451 ResearchApplying AI & Search in Europe - featuring 451 Research
Applying AI & Search in Europe - featuring 451 Research
Lucidworks
 
Webinar: Accelerate Data Science with Fusion 5.1
Webinar: Accelerate Data Science with Fusion 5.1Webinar: Accelerate Data Science with Fusion 5.1
Webinar: Accelerate Data Science with Fusion 5.1
Lucidworks
 
Webinar: 5 Must-Have Items You Need for Your 2020 Ecommerce Strategy
Webinar: 5 Must-Have Items You Need for Your 2020 Ecommerce StrategyWebinar: 5 Must-Have Items You Need for Your 2020 Ecommerce Strategy
Webinar: 5 Must-Have Items You Need for Your 2020 Ecommerce Strategy
Lucidworks
 
Where Search Meets Science and Style Meets Savings: Nordstrom Rack's Journey ...
Where Search Meets Science and Style Meets Savings: Nordstrom Rack's Journey ...Where Search Meets Science and Style Meets Savings: Nordstrom Rack's Journey ...
Where Search Meets Science and Style Meets Savings: Nordstrom Rack's Journey ...
Lucidworks
 
Apply Knowledge Graphs and Search for Real-World Decision Intelligence
Apply Knowledge Graphs and Search for Real-World Decision IntelligenceApply Knowledge Graphs and Search for Real-World Decision Intelligence
Apply Knowledge Graphs and Search for Real-World Decision Intelligence
Lucidworks
 
Webinar: Building a Business Case for Enterprise Search
Webinar: Building a Business Case for Enterprise SearchWebinar: Building a Business Case for Enterprise Search
Webinar: Building a Business Case for Enterprise Search
Lucidworks
 
Why Insight Engines Matter in 2020 and Beyond
Why Insight Engines Matter in 2020 and BeyondWhy Insight Engines Matter in 2020 and Beyond
Why Insight Engines Matter in 2020 and Beyond
Lucidworks
 

Mehr von Lucidworks (20)

Search is the Tip of the Spear for Your B2B eCommerce Strategy
Search is the Tip of the Spear for Your B2B eCommerce StrategySearch is the Tip of the Spear for Your B2B eCommerce Strategy
Search is the Tip of the Spear for Your B2B eCommerce Strategy
 
Drive Agent Effectiveness in Salesforce
Drive Agent Effectiveness in SalesforceDrive Agent Effectiveness in Salesforce
Drive Agent Effectiveness in Salesforce
 
How Crate & Barrel Connects Shoppers with Relevant Products
How Crate & Barrel Connects Shoppers with Relevant ProductsHow Crate & Barrel Connects Shoppers with Relevant Products
How Crate & Barrel Connects Shoppers with Relevant Products
 
Lucidworks & IMRG Webinar – Best-In-Class Retail Product Discovery
Lucidworks & IMRG Webinar – Best-In-Class Retail Product DiscoveryLucidworks & IMRG Webinar – Best-In-Class Retail Product Discovery
Lucidworks & IMRG Webinar – Best-In-Class Retail Product Discovery
 
Connected Experiences Are Personalized Experiences
Connected Experiences Are Personalized ExperiencesConnected Experiences Are Personalized Experiences
Connected Experiences Are Personalized Experiences
 
Intelligent Insight Driven Policing with MC+A, Toronto Police Service and Luc...
Intelligent Insight Driven Policing with MC+A, Toronto Police Service and Luc...Intelligent Insight Driven Policing with MC+A, Toronto Police Service and Luc...
Intelligent Insight Driven Policing with MC+A, Toronto Police Service and Luc...
 
[Webinar] Intelligent Policing. Leveraging Data to more effectively Serve Com...
[Webinar] Intelligent Policing. Leveraging Data to more effectively Serve Com...[Webinar] Intelligent Policing. Leveraging Data to more effectively Serve Com...
[Webinar] Intelligent Policing. Leveraging Data to more effectively Serve Com...
 
Preparing for Peak in Ecommerce | eTail Asia 2020
Preparing for Peak in Ecommerce | eTail Asia 2020Preparing for Peak in Ecommerce | eTail Asia 2020
Preparing for Peak in Ecommerce | eTail Asia 2020
 
Accelerate The Path To Purchase With Product Discovery at Retail Innovation C...
Accelerate The Path To Purchase With Product Discovery at Retail Innovation C...Accelerate The Path To Purchase With Product Discovery at Retail Innovation C...
Accelerate The Path To Purchase With Product Discovery at Retail Innovation C...
 
AI-Powered Linguistics and Search with Fusion and Rosette
AI-Powered Linguistics and Search with Fusion and RosetteAI-Powered Linguistics and Search with Fusion and Rosette
AI-Powered Linguistics and Search with Fusion and Rosette
 
The Service Industry After COVID-19: The Soul of Service in a Virtual Moment
The Service Industry After COVID-19: The Soul of Service in a Virtual MomentThe Service Industry After COVID-19: The Soul of Service in a Virtual Moment
The Service Industry After COVID-19: The Soul of Service in a Virtual Moment
 
Webinar: Smart answers for employee and customer support after covid 19 - Europe
Webinar: Smart answers for employee and customer support after covid 19 - EuropeWebinar: Smart answers for employee and customer support after covid 19 - Europe
Webinar: Smart answers for employee and customer support after covid 19 - Europe
 
Smart Answers for Employee and Customer Support After COVID-19
Smart Answers for Employee and Customer Support After COVID-19Smart Answers for Employee and Customer Support After COVID-19
Smart Answers for Employee and Customer Support After COVID-19
 
Applying AI & Search in Europe - featuring 451 Research
Applying AI & Search in Europe - featuring 451 ResearchApplying AI & Search in Europe - featuring 451 Research
Applying AI & Search in Europe - featuring 451 Research
 
Webinar: Accelerate Data Science with Fusion 5.1
Webinar: Accelerate Data Science with Fusion 5.1Webinar: Accelerate Data Science with Fusion 5.1
Webinar: Accelerate Data Science with Fusion 5.1
 
Webinar: 5 Must-Have Items You Need for Your 2020 Ecommerce Strategy
Webinar: 5 Must-Have Items You Need for Your 2020 Ecommerce StrategyWebinar: 5 Must-Have Items You Need for Your 2020 Ecommerce Strategy
Webinar: 5 Must-Have Items You Need for Your 2020 Ecommerce Strategy
 
Where Search Meets Science and Style Meets Savings: Nordstrom Rack's Journey ...
Where Search Meets Science and Style Meets Savings: Nordstrom Rack's Journey ...Where Search Meets Science and Style Meets Savings: Nordstrom Rack's Journey ...
Where Search Meets Science and Style Meets Savings: Nordstrom Rack's Journey ...
 
Apply Knowledge Graphs and Search for Real-World Decision Intelligence
Apply Knowledge Graphs and Search for Real-World Decision IntelligenceApply Knowledge Graphs and Search for Real-World Decision Intelligence
Apply Knowledge Graphs and Search for Real-World Decision Intelligence
 
Webinar: Building a Business Case for Enterprise Search
Webinar: Building a Business Case for Enterprise SearchWebinar: Building a Business Case for Enterprise Search
Webinar: Building a Business Case for Enterprise Search
 
Why Insight Engines Matter in 2020 and Beyond
Why Insight Engines Matter in 2020 and BeyondWhy Insight Engines Matter in 2020 and Beyond
Why Insight Engines Matter in 2020 and Beyond
 

Kürzlich hochgeladen

Perth MuleSoft Meetup July 2024
Perth MuleSoft Meetup July 2024Perth MuleSoft Meetup July 2024
Perth MuleSoft Meetup July 2024
Michael Price
 
Accelerating Migrations = Recommendations
Accelerating Migrations = RecommendationsAccelerating Migrations = Recommendations
Accelerating Migrations = Recommendations
isBullShit
 
The History of Embeddings & Multimodal Embeddings
The History of Embeddings & Multimodal EmbeddingsThe History of Embeddings & Multimodal Embeddings
The History of Embeddings & Multimodal Embeddings
Zilliz
 
Zaitechno Handheld Raman Spectrometer.pdf
Zaitechno Handheld Raman Spectrometer.pdfZaitechno Handheld Raman Spectrometer.pdf
Zaitechno Handheld Raman Spectrometer.pdf
AmandaCheung15
 
COVID-19 and the Level of Cloud Computing Adoption: A Study of Sri Lankan Inf...
COVID-19 and the Level of Cloud Computing Adoption: A Study of Sri Lankan Inf...COVID-19 and the Level of Cloud Computing Adoption: A Study of Sri Lankan Inf...
COVID-19 and the Level of Cloud Computing Adoption: A Study of Sri Lankan Inf...
AimanAthambawa1
 
BLOCKCHAIN TECHNOLOGY - Advantages and Disadvantages
BLOCKCHAIN TECHNOLOGY - Advantages and DisadvantagesBLOCKCHAIN TECHNOLOGY - Advantages and Disadvantages
BLOCKCHAIN TECHNOLOGY - Advantages and Disadvantages
SAI KAILASH R
 
Intel Unveils Core Ultra 200V Lunar chip .pdf
Intel Unveils Core Ultra 200V Lunar chip .pdfIntel Unveils Core Ultra 200V Lunar chip .pdf
Intel Unveils Core Ultra 200V Lunar chip .pdf
Tech Guru
 
Acumatica vs. Sage Intacct _Construction_July (1).pptx
Acumatica vs. Sage Intacct _Construction_July (1).pptxAcumatica vs. Sage Intacct _Construction_July (1).pptx
Acumatica vs. Sage Intacct _Construction_July (1).pptx
BrainSell Technologies
 
Girls call Kolkata 👀 XXXXXXXXXXX 👀 Rs.9.5 K Cash Payment With Room Delivery
Girls call Kolkata 👀 XXXXXXXXXXX 👀 Rs.9.5 K Cash Payment With Room Delivery Girls call Kolkata 👀 XXXXXXXXXXX 👀 Rs.9.5 K Cash Payment With Room Delivery
Girls call Kolkata 👀 XXXXXXXXXXX 👀 Rs.9.5 K Cash Payment With Room Delivery
sunilverma7884
 
UX Webinar Series: Essentials for Adopting Passkeys as the Foundation of your...
UX Webinar Series: Essentials for Adopting Passkeys as the Foundation of your...UX Webinar Series: Essentials for Adopting Passkeys as the Foundation of your...
UX Webinar Series: Essentials for Adopting Passkeys as the Foundation of your...
FIDO Alliance
 
kk vathada _digital transformation frameworks_2024.pdf
kk vathada _digital transformation frameworks_2024.pdfkk vathada _digital transformation frameworks_2024.pdf
kk vathada _digital transformation frameworks_2024.pdf
KIRAN KV
 
Uncharted Together- Navigating AI's New Frontiers in Libraries
Uncharted Together- Navigating AI's New Frontiers in LibrariesUncharted Together- Navigating AI's New Frontiers in Libraries
Uncharted Together- Navigating AI's New Frontiers in Libraries
Brian Pichman
 
Computer HARDWARE presenattion by CWD students class 10
Computer HARDWARE presenattion by CWD students class 10Computer HARDWARE presenattion by CWD students class 10
Computer HARDWARE presenattion by CWD students class 10
ankush9927
 
Garbage In, Garbage Out: Why poor data curation is killing your AI models (an...
Garbage In, Garbage Out: Why poor data curation is killing your AI models (an...Garbage In, Garbage Out: Why poor data curation is killing your AI models (an...
Garbage In, Garbage Out: Why poor data curation is killing your AI models (an...
Zilliz
 
Semantic-Aware Code Model: Elevating the Future of Software Development
Semantic-Aware Code Model: Elevating the Future of Software DevelopmentSemantic-Aware Code Model: Elevating the Future of Software Development
Semantic-Aware Code Model: Elevating the Future of Software Development
Baishakhi Ray
 
Camunda Chapter NY Meetup July 2024.pptx
Camunda Chapter NY Meetup July 2024.pptxCamunda Chapter NY Meetup July 2024.pptx
Camunda Chapter NY Meetup July 2024.pptx
ZachWylie3
 
Redefining Cybersecurity with AI Capabilities
Redefining Cybersecurity with AI CapabilitiesRedefining Cybersecurity with AI Capabilities
Redefining Cybersecurity with AI Capabilities
Priyanka Aash
 
Retrieval Augmented Generation Evaluation with Ragas
Retrieval Augmented Generation Evaluation with RagasRetrieval Augmented Generation Evaluation with Ragas
Retrieval Augmented Generation Evaluation with Ragas
Zilliz
 
Communications Mining Series - Zero to Hero - Session 3
Communications Mining Series - Zero to Hero - Session 3Communications Mining Series - Zero to Hero - Session 3
Communications Mining Series - Zero to Hero - Session 3
DianaGray10
 
Generative AI Reasoning Tech Talk - July 2024
Generative AI Reasoning Tech Talk - July 2024Generative AI Reasoning Tech Talk - July 2024
Generative AI Reasoning Tech Talk - July 2024
siddu769252
 

Kürzlich hochgeladen (20)

Perth MuleSoft Meetup July 2024
Perth MuleSoft Meetup July 2024Perth MuleSoft Meetup July 2024
Perth MuleSoft Meetup July 2024
 
Accelerating Migrations = Recommendations
Accelerating Migrations = RecommendationsAccelerating Migrations = Recommendations
Accelerating Migrations = Recommendations
 
The History of Embeddings & Multimodal Embeddings
The History of Embeddings & Multimodal EmbeddingsThe History of Embeddings & Multimodal Embeddings
The History of Embeddings & Multimodal Embeddings
 
Zaitechno Handheld Raman Spectrometer.pdf
Zaitechno Handheld Raman Spectrometer.pdfZaitechno Handheld Raman Spectrometer.pdf
Zaitechno Handheld Raman Spectrometer.pdf
 
COVID-19 and the Level of Cloud Computing Adoption: A Study of Sri Lankan Inf...
COVID-19 and the Level of Cloud Computing Adoption: A Study of Sri Lankan Inf...COVID-19 and the Level of Cloud Computing Adoption: A Study of Sri Lankan Inf...
COVID-19 and the Level of Cloud Computing Adoption: A Study of Sri Lankan Inf...
 
BLOCKCHAIN TECHNOLOGY - Advantages and Disadvantages
BLOCKCHAIN TECHNOLOGY - Advantages and DisadvantagesBLOCKCHAIN TECHNOLOGY - Advantages and Disadvantages
BLOCKCHAIN TECHNOLOGY - Advantages and Disadvantages
 
Intel Unveils Core Ultra 200V Lunar chip .pdf
Intel Unveils Core Ultra 200V Lunar chip .pdfIntel Unveils Core Ultra 200V Lunar chip .pdf
Intel Unveils Core Ultra 200V Lunar chip .pdf
 
Acumatica vs. Sage Intacct _Construction_July (1).pptx
Acumatica vs. Sage Intacct _Construction_July (1).pptxAcumatica vs. Sage Intacct _Construction_July (1).pptx
Acumatica vs. Sage Intacct _Construction_July (1).pptx
 
Girls call Kolkata 👀 XXXXXXXXXXX 👀 Rs.9.5 K Cash Payment With Room Delivery
Girls call Kolkata 👀 XXXXXXXXXXX 👀 Rs.9.5 K Cash Payment With Room Delivery Girls call Kolkata 👀 XXXXXXXXXXX 👀 Rs.9.5 K Cash Payment With Room Delivery
Girls call Kolkata 👀 XXXXXXXXXXX 👀 Rs.9.5 K Cash Payment With Room Delivery
 
UX Webinar Series: Essentials for Adopting Passkeys as the Foundation of your...
UX Webinar Series: Essentials for Adopting Passkeys as the Foundation of your...UX Webinar Series: Essentials for Adopting Passkeys as the Foundation of your...
UX Webinar Series: Essentials for Adopting Passkeys as the Foundation of your...
 
kk vathada _digital transformation frameworks_2024.pdf
kk vathada _digital transformation frameworks_2024.pdfkk vathada _digital transformation frameworks_2024.pdf
kk vathada _digital transformation frameworks_2024.pdf
 
Uncharted Together- Navigating AI's New Frontiers in Libraries
Uncharted Together- Navigating AI's New Frontiers in LibrariesUncharted Together- Navigating AI's New Frontiers in Libraries
Uncharted Together- Navigating AI's New Frontiers in Libraries
 
Computer HARDWARE presenattion by CWD students class 10
Computer HARDWARE presenattion by CWD students class 10Computer HARDWARE presenattion by CWD students class 10
Computer HARDWARE presenattion by CWD students class 10
 
Garbage In, Garbage Out: Why poor data curation is killing your AI models (an...
Garbage In, Garbage Out: Why poor data curation is killing your AI models (an...Garbage In, Garbage Out: Why poor data curation is killing your AI models (an...
Garbage In, Garbage Out: Why poor data curation is killing your AI models (an...
 
Semantic-Aware Code Model: Elevating the Future of Software Development
Semantic-Aware Code Model: Elevating the Future of Software DevelopmentSemantic-Aware Code Model: Elevating the Future of Software Development
Semantic-Aware Code Model: Elevating the Future of Software Development
 
Camunda Chapter NY Meetup July 2024.pptx
Camunda Chapter NY Meetup July 2024.pptxCamunda Chapter NY Meetup July 2024.pptx
Camunda Chapter NY Meetup July 2024.pptx
 
Redefining Cybersecurity with AI Capabilities
Redefining Cybersecurity with AI CapabilitiesRedefining Cybersecurity with AI Capabilities
Redefining Cybersecurity with AI Capabilities
 
Retrieval Augmented Generation Evaluation with Ragas
Retrieval Augmented Generation Evaluation with RagasRetrieval Augmented Generation Evaluation with Ragas
Retrieval Augmented Generation Evaluation with Ragas
 
Communications Mining Series - Zero to Hero - Session 3
Communications Mining Series - Zero to Hero - Session 3Communications Mining Series - Zero to Hero - Session 3
Communications Mining Series - Zero to Hero - Session 3
 
Generative AI Reasoning Tech Talk - July 2024
Generative AI Reasoning Tech Talk - July 2024Generative AI Reasoning Tech Talk - July 2024
Generative AI Reasoning Tech Talk - July 2024
 

Webinar: Modern Techniques for Better Search Relevance with Fusion

  • 1. Modern  Techniques  for  Better  Search   Relevance  in  Fusion Grant  Ingersoll   CTO,  Lucidworks   November  15,  2017
  • 8. So,  what  do  you  do?
  • 10. if  (doc.name.contains(“Vikings”)){   doc.boost  =  100   } OR q:(MAIN  QUERY)  OR  (name:Vikings)^10 Index  Time: Query  Time:
  • 13. • Term  Frequency:  “How  well  a  term  describes  a  document”   • Measure:  how  often  a  term  occurs  per  document   • Inverse  Document  Frequency:  “How  important  is  a  term   overall”   • Measure:  how  rare  the  term  is  across  all  documents TF*IDF
  • 14. Score(q,  d)  =                  ∑    idf(t)  ·∙  (  tf(t  in  d)  ·∙  (k  +  1)  )  /  (  tf(t  in  d)  +  k  ·∙  (1  –  b  +  b  ·∙  |d|  /  avgdl  )                        t  in  q   Where:                      t  =  term;  d  =  document;  q  =  query;  i  =  index
                  tf(t  in  d)    =    numTermOccurrencesInDocument  ½                    idf(t)  =    1  +  log  (numDocs  /  (docFreq  +  1))                    |d|  =    ∑  1                                                            t  in  d                    avgdl  =  =  (  ∑  |d|    )  /  (  ∑  1  )  )                                                                                  d  in  i                              d  in  i                    k  =  Free  parameter.  Usually  ~1.2  to  2.0.  Increases  term  frequency  saturation  point.                    b  =  Free  parameter.  Usually  ~0.75.  Increases  impact  of  document  normalization. BM25  (aka  Okapi)
  • 15. • Capture  and  log  pretty  much  everything   • Searches,  clicks,  time  on  page,  seen/not,  etc.   • Precision  —  Of  those  shown,  what’s  relevant?   • Recall  —  Of  all  that’s  relevant,  what  was  found?   • NDCG  —  Account  for  position Measure,  Measure,  Measure
  • 18. 💡
  • 19. WWGD?
  • 20. 👮 ♀👜 👲 💼 !♀😎 🎓 🎅 "👸 🚴 💁 ♂🚜 !🙎 ♂👮 ♀👜 👲 💼 !♀😎 🎓 🎅 "👸 🚴 💁 ♂🚜 !🙎 ♂🚵 ♀!💆 0 💃 🔬 !♀👵 🏃 ♀💂 👔 8 🚶 🚵 ♀!💆 0 👮 1 👮 ♀👜 👲 💼 !♀😎 🎓 🎅 "👸 🚴 💁 ♂🚜 !🙎 ♂🚵 ♀!💆 0 "🏇 "♀🏈 "💆 ♂🏄 ♀💇 👷 !👷 ♀🏂 💇 ♂👴 👼 💃 🔬 !♀👵 🏃 ♀💂 👔 8 🚶 🚵 ♀!💆 0 👮 1 🚵 ♀!💆 0 !🎪 🚶 ♀👮 ♀👜 👲 💼 !♀😎 🎓 🎅 "👸 🚴 💁 ♂🚜 !🙎 ♂💃 🔬 !♀👵 🏃 ♀💂 👔 8 🚶 🚵 ♀!💆 0 👮 🚵 ♀! 💃 🔬 !♀👵 🏃 ♀💂 👔 8 🚶 🚵 ♀!💆 0 👮 1 💃 🔬 !♀👵 🏃 ♀💂 👔 8 🚶 🚵 ♀!💆 0 👮 1 🚵 ♀!💆 0 🏇 "♀🏈 "💆 ♂🏄 ♀💇 👷 !👷 ♀🏂 💇 ♂👴 👼 💂 ♀B 🎒 !🎪 🚶 ♀💃 🔬 !♀👵 🏃 ♀💂 👔 8 🚶 🚵 ♀!💆 0
  • 21. Magic Guessing Core  Information  Theory  (aka  Lucene/Solr) Search  Aids  (Facets,  Did  You  Mean,  Highlighting) Machine  Learning/NLP     (Clicks,  Crowd  Sourcing,  Recs,  Personalization,  User  feedback) Rules,  Domain  Specific  Knowledge fuhgeddaboudit
  • 22. Content Collabora*on Context Core  Solr  capabili*es:  text  matching,  face*ng,   spell  checking,  highligh*ng   Business  Rules  for  content:  landing  pages,  boost/ block,  promopons,  etc. Leverage  collec*ve  intelligence  to  predict  what   users  will  do  based  on  historical,  aggregated   data   Recommenders,  Popularity,  Search  Paths Who  are  you?    Where  are  you?    What  have  you   done  previously?   User/Market  Segmentapon,  Roles,  Security,   Personalizapon Next Generation Relevance
  • 23. But  What  About  the  Real  World?  Indexing  Edipon Machine  Learning/ NLP   NER,  Topic  Detection,   Clustering  Word2Vec,  etc. Domain  Rules:   Synonyms,  Regexes,   Lexical  Resources Extraction Load  Into  Spark Build  W2V,   PageRank,  Topic,   Clustering  Models Offline Content Models
  • 24. Real  World?  Query  Edipon Query  Intent     Strategic,  Tactical,   Semantic😊 iPad case Head/Tail/ Clickstream/ Recommenders User  Factors:   Segmentation,  Location,   History,  Profile,  Security Parse Domain  Specific   Rules Transform  Results … Cascading  Rerankers   Learn  To  Rank  (multi-­‐ model),  Bias  corrections
  • 25. Real  World?  Users  Edipon Load  Into  SparkSignals Query  Analysis Recommenders/ Personalization 😊 iPad case Query  Edition Raw Models Clickstream  Models
  • 26. (Exact/Original  Match)^X     (Sloppy  Phrase)~M^Y     (AND  Q)^Z     (OR  Q)^XX   (Expansions/Click/Head/Tail  Boosts)^YY   (Personalization  Biases)^ZZ   ({!ltr  model=…})   Filters+Options:  security,  rules,  hard  preferences,  categories The  Perfect(?!?)  Query*    YMMV! }  Precision Recall Caveat  Emptor! *  Note:  there  are  a  lot  of  variations  on  this.    edismax  handles  most Learn  to  Rank X  >  Y  >  Z  >  XX   All  weights  can  be  learned
  • 27. • Don’t  take  my  word  for  it,  experiment!   • A/B  Tests,  Multi-­‐arm  Bandits   • Good  primer:     • http://www.slideshare.net/InfoQ/online-­‐controlled-­‐experiments-­‐introduction-­‐ insights-­‐scaling-­‐and-­‐humbling-­‐statistics   • Rules  are  fine,  as  long  as  the  are  contained,  have  a  lifespan   and  are  measured  for  effectiveness Experimentapon,  Not  Editorializapon
  • 28. Show  Us  Already,  Will  You!
  • 29. 29 Lucidworks Fusion Product Suite The Lucidworks platform provides all of the components needed to create and 
 run smart enterprise and consumer applications Create rich UI with modular components for web and mobile Surface the insights that matter most with the power of machine learning and artificial intelligence Highly scalable search engine and NoSQL datastore that gives you instant access to all your data Combine the power of the Fusion stack with the simplicity you’d expect in a SaaS-based application
  • 30. Lucidworks Fusion Architecture Web App Mobile BI/Analytics Logs File Web Database Box/ Dropbox Elasticsearch SDK Sharepoint Slack Jive Connectors Admin UI Search Analytics Visualization REST/ SQL Hadoop Google Drive Security Built In Proven Speed CDCR Extensible Scalable Responsive NLP: NER, Phrases, POS Query Intent & Doc Classification Recommenders Anomaly Detection Signals and Query Analytics Clustering A/B Testing ETL and Query Pipelines Alerting and Messaging SQL and Catalog Scheduling Connectors/Federation Import/Export Custom Jobs RulesTopic Detection Custom Search Devs Data Scientists Business Users Cross Cutting Features HDFS (Optional)
  • 31. Fusion: Meeting the Search Challenge Relevance and Discovery Business Support Intelligence Open & Scalable Signal Proc. Machine Learning NLP Math/Stats Proven Search Extensible Simplified DevOps Real Time Query/Doc Simulations Rules Analytics User Interface Personalization Recommendations Query Intent Experimentation (A/B)
  • 32. Demo Details • Ecommerce  Data  Set   -­‐ Product  Catalog:  ~1.3M   -­‐ Signals:  1  month  of  query,  document  logs   • Fusion  3.1  +  Rules  (open  source  add-­‐on  module)  +  Solr  LTR  contrib   • Spark  2.x,  Solr  6.5   • App  Studio  UI  (hyp://twigkit.com) Demo  Details
  • 33. • http://lucidworks.com   • grant@   • http://lucene.apache.org/solr   • http://spark.apache.org/   • https://github.com/lucidworks/spark-­‐solr   • https://cwiki.apache.org/confluence/display/solr/Learning+To+Rank   • Bloomberg  talk  on  LTR  https://www.youtube.com/watch? v=M7BKwJoh96s Resources