The Intent Algorithms of Search & Recommendation Engines

Trey Grainger
SVP of Engineering @ Lucidworks
The Intent Algorithms of
Search & Recommendation Engines
Furman University: CSC272 Guest Lecture
2017.12.01

Trey Grainger
SVP of Engineering
• Previously Director of Engineering @ CareerBuilder
• MBA, Management of Technology – Georgia Tech
• BA, Computer Science, Business, & Philosophy – Furman University
• Information Retrieval & Web Search - Stanford University
Other fun projects:
• Co-author of Solr in Action, plus numerous research papers
• Frequent conference speaker
• Founder of Celiaccess.com, the gluten-free search engine
• Lucene/Solr contributor
• Startup Advisor (including Presearch, the decentralized web search engine)
About Me

• 1. Teach the fundamentals of how a search engine works
- Inverted Index
- Ranking
• 2. Discuss Real-world use cases
• 3. Walk through how search / recommendation
engines continuously learn
from user behavior and content
• 4. Fun Examples
Goals
…
• Reflected Intelligence
- Signals
-Recommendations
- Learning to Rank (Demo)
• Semantic Search
- Entity Extraction
- Query Parsing
- Knowledge Graphs

Traditional
Keyword
Search
Recommendations
Semantic
Search
User Intent
Personalized
Search
Augmented
Search
Domain-aware
Matching
Dimensions of
User Intent

Based in San Francisco, offices
and employees worldwide
Over 300 customers across the
Fortune 1000
Fusion, a Solr-powered platform
for search-driven apps
Consulting and support for
organizations using Solr
Produces the world’s largest open
source user conference dedicated
to Lucene/Solr
Lucidworks is the primary commercial
contributor to the Apache Solr project
Employs over 40% of the active
committers on the Solr project
Contributes over 70% of Solr's
open source codebase
40%
70%

Search-Driven
Everything
Customer
Service
Customer
Insights
Fraud Surveillance
Research
Portal
Online Retail
Digital
Content

Highly scalable
search engine and
NoSQL datastore that
gives you instant
access to all your
data.
Surface the insights
that matter most with
the power of machine
learning and artificial
intelligence
Create bespoke data and
discovery applications with
modular UI components for
web and mobile.
The Lucidworks platform provides all of the components needed to design, develop
and deploy smart search experiences for Enterprise and Consumer search
applications
Lucidworks Fusion product suite
Fusion Cloud
combines the power of
Fusion with the
simplicity you’d expect
in a SaaS-based
application.

Fusion powers search for the brightest companies in the
world.

“Solr is the popular, blazing-fast,
open source enterprise search
platform built on Apache Lucene™.”

Key Solr Features:
● Multilingual Keyword search
● Relevancy Ranking of results
● Faceting & Analytics (nested / relational)
● Highlighting
● Spelling Correction
● Autocomplete/Type-ahead Prediction
● Sorting, Grouping, Deduplication
● Distributed, Fault-tolerant, Scalable
● Geospatial search
● Complex Function queries
● Recommendations (More Like This)
● Graph Queries and Traversals
● SQL Query Support
● Streaming Aggregations
● Batch and Streaming processing
● Highly Configurable / Plugins
● Learning to Rank
● Building machine-learning models
● … many more
*source: Solr in Action, chapter 2

The standard
for enterprise
search.
of Fortune 500
uses Solr.
90%

Basic Keyword Search
(inverted index, tf-idf, bm25,
multilingual text analysis, query
formulation, etc.)
Taxonomies /
Entity Extraction
(entity recognition,
ontologies, synonyms, etc.)
Query Intent
(query classification, semantic
query parsing, concept
expansion, rules, clustering,
classification)
Relevancy Tuning
(signals, AB testing/genetic
algorithms, Learning to Rank,
Neural Networks)
Self-learning
Intent Algorithm Spectrum

Basic Keyword Search
The beginning of a typical search journey

Term Documents
a doc1 [2x]
brown doc3 [1x] , doc5 [1x]
cat doc4 [1x]
cow doc2 [1x] , doc5 [1x]
… ...
once doc1 [1x], doc5 [1x]
over doc2 [1x], doc3 [1x]
the doc2 [2x], doc3 [2x],
doc4[2x], doc5 [1x]
… …
Document Content Field
doc1 once upon a time, in a land far,
far away
doc2 the cow jumped over the moon.
doc3 the quick brown fox jumped over
the lazy dog.
doc4 the cat in the hat
doc5 The brown cow said “moo”
once.
… …
What you SEND to Lucene/Solr:
How the content is INDEXED into
Lucene/Solr (conceptually):
The inverted index

/solr/select/?q=apache solr
Field Documents
… …
apache doc1, doc3, doc4,
doc5
…
hadoop doc2, doc4, doc6
… …
solr doc1, doc3, doc4,
doc7, doc8
… …
doc5
doc7 doc8
doc1 doc3
doc4
solr
apache
apache solr
Matching queries to documents

Text Analysis
Generating terms to index from raw text

Text Analysis in Solr
A text field in Lucene/Solr has an Analyzer containing:
① Zero or more CharFilters
Takes incoming text and “cleans it up”
before it is tokenized
② One Tokenizer
Splits incoming text into a Token Stream
containing Zero or more Tokens
③ Zero or more TokenFilters
Examines and optionally modifies each
Token in the Token Stream
*From Solr in Action, Chapter 6

A text field in Lucene/Solr has an Analyzer containing:
① Zero or more CharFilters
Takes incoming text and “cleans it up”
before it is tokenized
② One Tokenizer
Splits incoming text into a Token Stream
containing Zero or more Tokens
③ Zero or more TokenFilters
Examines and optionally modifies each
Token in the Token Stream
Text Analysis in Solr
*From Solr in Action, Chapter 6

Per-language Analysis Chains
*Some of the 32 different languages configurations in Appendix B of Solr in Action

Relevancy Ranking
Scoring the results, returning the best matches

Classic Lucene/Solr Relevancy Algorithm:
*Source: Solr in Action, chapter 3
Score(q, d) =
∑ ( tf(t in d) · idf(t)2 · t.getBoost() · norm(t, d) ) · coord(q, d) · queryNorm(q)
t in q
Where:
t = term; d = document; q = query; f = field
tf(t in d) = numTermOccurrencesInDocument ½
idf(t) = 1 + log (numDocs / (docFreq + 1))
coord(q, d) = numTermsInDocumentFromQuery / numTermsInQuery
queryNorm(q) = 1 / (sumOfSquaredWeights ½ )
sumOfSquaredWeights = q.getBoost()2 · ∑ (idf(t) · t.getBoost() )2
t in q
norm(t, d) = d.getBoost() · lengthNorm(f) · f.getBoost()

• 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

BM25 (Okapi “Best Match” 25th Iteration)
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.

News Search : popularity and freshness drive relevance
Restaurant Search: geographical proximity and price range are critical
Ecommerce: likelihood of a purchase is key
Movie search: More popular titles are generally more relevant
Job search: category of job, salary range, and geographical proximity matter
TF * IDF of keywords can’t hold it’s own against good
domain-specific relevance factors!
That’s great, but what about domain-specific knowledge?

*Example from chapter 16 of Solr in Action
Domain-specific relevancy calculation (News Website Example)
News website:
/select?
fq=$myQuery&
q=_query_:"{!func}scale(query($myQuery),0,100)"
AND _query_:"{!func}div(100,map(geodist(),0,1,1))"
AND _query_:"{!func}recip(rord(publicationDate),0,100,100)"
AND _query_:"{!func}scale(popularity,0,100)"&
myQuery="street festival"&
sfield=location&
pt=33.748,-84.391
25%
25%
25%
25%

Fancy boosting functions (Restaurant Search Example)
Distance (50%) + keywords (30%) + category (20%)
q=_val_:"scale(mul(query($keywords),1),0,30)" AND
_val_:"scale(sum($radiusInKm,mul(query($distance),-1)),0,50)” AND
_val_:"scale(mul(query($category),1),0,20)"
&keywords=filet mignon
&radiusInKm=48.28
&distance=_val_:"geodist(latitudelongitude.latlon_is,33.77402,-84.29659)”
&category=”fine dining"
&fq={!cache=false v=$keywords}

This is powerful, but feels like
a lot of work to get right…

what is “reflected intelligence”?

The Three C’s
Content:
Keywords and other features in your documents
Collaboration:
How other’s have chosen to interact with your system
Context:
Available information about your users and their intent
Reflected Intelligence
“Leveraging previous data and interactions to improve how
new data and interactions should be interpreted”

● Recommendation Algorithms
● Building user profiles from past searches, clicks, and other actions
● Identifying correlations between keywords/phrases
● Building out automatically-generated ontologies from content and queries
● Determining relevancy judgements (precision, recall, nDCG, etc.) from click
logs
● Learning to Rank - using relevancy judgements and machine learning to train
a relevance model
● Discovering misspellings, synonyms, acronyms, and related keywords
● Disambiguation of keyword phrases with multiple meanings
● Learning what’s important in your content
Examples of Reflected Intelligence

John lives in Boston but wants to move to New York or possibly another big city. He is
currently a sales manager but wants to move towards business development.
Irene is a bartender in Dublin and is only interested in jobs within 10KM of her location
in the food service industry.
Irfan is a software engineer in Atlanta and is interested in software engineering jobs at a
Big Data company. He is happy to move across the U.S. for the right job.
Jane is a nurse educator in Boston seeking between $40K and $60K
Consider what you know about users

http://localhost:8983/solr/jobs/select/?
fl=jobtitle,city,state,salary&
q=(
jobtitle:"nurse educator"^25 OR jobtitle:(nurse educator)^10
)
AND (
(city:"Boston" AND state:"MA")^15
OR state:"MA")
AND _val_:"map(salary, 40000, 60000,10, 0)”
Query for Jane
Jane is a nurse educator in Boston seeking between $40K and $60K

{ ...
"response":{"numFound":22,"start":0,"docs":[
{"jobtitle":" Clinical Educator
(New England/ Boston)",
"city":"Boston",
"state":"MA",
"salary":41503},
…]}}
*Example documents available @ http://github.com/treygrainger/solr-in-action
Search Results for Jane
{"jobtitle":"Nurse Educator",
"city":"Braintree",
"state":"MA",
"salary":56183},
{"jobtitle":"Nurse Educator",
"city":"Brighton",
"state":"MA",
"salary":71359}

You just built a
recommendation engine!

Collaborative Filtering
Term Documents
user1 doc1, doc5
user2 doc2
user3 doc2
user4 doc1, doc3,
doc4, doc5
user5 doc1, doc4
… …
Document “Users who bought this product” field
doc1 user1, user4, user5
doc2 user2, user3
doc3 user4
doc4 user4, user5
doc5 user4, user1
… …
What you SEND to Lucene/Solr:
How the content is INDEXED into
Lucene/Solr (conceptually):

Step 1: Find similar users who like the same documents
Document “Users who bought this product” field
doc1 user1, user4, user5
doc2 user2, user3
doc3 user4
doc4 user4, user5
doc5 user4, user1
… …
Top-scoring results (most similar users):
1) user4 (2 shared likes)
3) user 1 (1 shared like)
doc1
user1 user4
user5
user4 user5
doc4
q=documentid: ("doc1" OR "doc4")

/solr/select/?q=userlikes:("user4"^2
OR "user5"^2 OR "user1"^1)
Step 2: Search for docs “liked” by those similar users
Term Documents
user1 doc1, doc5
user2 doc2
user3 doc2
user4 doc1, doc3,
doc4, doc5
user5 doc1, doc4
… …
Top recommended documents:
1) doc1 (matches user4, user5, user1)
2) doc4 (matches user4, user5)
3) doc5 (matches user4, user1)
4) doc3 (matches user4)
// doc2 does not match
Most similar users:
3) user 1 (1 shared like)

Using matrix factorization is typically more efficient (but less real-time)

Feedback Loops
User
Searches
User
Sees
Results
User
takes an
action
Users’ actions
inform system
improvements

User
Searches
User
Sees
Results
User
takes an
action

Demo:
Signals & Recommendations

• 200%+ increase in
click-through rates
• 91% lower TCO
• 50,000 fewer support
tickets
• Increased customer
satisfaction

Full text search on Mobile
• 5 percent of visitors use
Search
• They account for 15% of
sales.
• Fusion increases
(pageviews) engagement by
63%
• Bounce rate decreased 8%
• Search users convert to
reservation/ticket - 33%
higher

Retail search for a home
improvement store chain
• Clicks increased 12.16%
• Add to Cart increased
16.94%
• Checkout increased 18.35%
• Fusion will add an
incremental $6.5M to the
checkout

We saw a 50-60% increase in
conversions
just from turning on Fusion”
“
Jacob Wagner, Director of IT - Content,
Bluestem Brands
—

We saw a 50-60% increase in
conversions
just from turning on Fusion”
“
Jacob Wagner, Director of IT - Content,
Bluestem Brands
—
-160 million annual
website visits
-2.61 million square foot
distribution space
-$2+ billion in annual revenue

3rd largest bank in the US
• 2016: 38 Million Searches.
293K Unique Users
• .014% Zero Result
Searches
• Employee Survey: Search is
the #1 most valuable feature
of their intranet

The Semantic Search Problem
User’s Query:
machine learning research and development Portland, OR software
engineer AND hadoop, java
Traditional Query Parsing:
(machine AND learning AND research AND development AND portland)
OR (software AND engineer AND hadoop AND java)
Semantic Query Parsing:
"machine learning" AND "research and development" AND "Portland, OR"
AND "software engineer" AND hadoop AND java
Semantically Expanded Query:
("machine learning"^10 OR "data scientist" OR "data mining" OR "artificial intelligence")
AND ("research and development"^10 OR "r&d") AND
AND ("Portland, OR"^10 OR "Portland, Oregon" OR {!geofilt pt=45.512,-122.676 d=50 sfield=geo})
AND ("software engineer"^10 OR "software developer")
AND (hadoop^10 OR "big data" OR hbase OR hive) AND (java^10 OR j2ee)

Building a Taxonomy of Entities
Many ways to generate this:
• Statistical Analysis of interesting phrases
- Word2Vec / Glove
• Topic Modelling
• Clustering of documents / phrases
• Buy a dictionary (often doesn’t work for
domain-specific search problems)
• Generate a model of domain-specific phrases by
mining query logs for commonly searched phrases within the domain*
* K. Aljadda, M. Korayem, T. Grainger, C. Russell. "Crowdsourced Query Augmentation through Semantic Discovery of Domain-specific Jargon," in IEEE Big Data 2014.

Probabilistic Query Parser
Goal: given a query, predict which
combinations of keywords should be
combined together as phrases
Example:
senior java developer hadoop
Possible Parsings:
senior, java, developer, hadoop
"senior java", developer, hadoop
"senior java developer", hadoop
"senior java developer hadoop”
"senior java", "developer hadoop”
senior, "java developer", hadoop
senior, java, "developer hadoop" Source: Trey Grainger, “Searching on Intent: Knowledge Graphs, Personalization,
and Contextual Disambiguation”, Bay Area Search Meetup, November 2015.

Semantic Query Parsing
Identification of phrases in queries using two steps:
1) Check a dictionary of known terms that is continuously
built, cleaned, and refined based upon common inputs from
interactions with real users of the system. The SolrTextTagger
works well for this.*
2) Also invoke a probabilistic query parser to dynamically
identify unknown phrases using statistics from a corpus of data
(language model)
*K. Aljadda, M. Korayem, T. Grainger, C. Russell. "Crowdsourced Query Augmentation
through Semantic Discovery of Domain-specific Jargon," in IEEE Big Data 2014.

A Graph
DSAA 2016
Montreal
Quebec Canada
Semantic
Knowledge
Graph Paper
Trey
Grainger
Mohammed
Korayem
Andries
Smith
Khalifeh
AlJadda
in_country
Node / Vertex
Edge

id: 1
job_title: Software Engineer
desc: software engineer at a
great company
skills: .Net, C#, java
id: 2
job_title: Registered Nurse
desc: a registered nurse at
hospital doing hard work
skills: oncology, phlebotemy
id: 3
job_title: Java Developer
desc: a software engineer or a
java engineer doing work
skills: java, scala, hibernate
field term postings list
doc pos
desc
a
1 4
2 1
3 1, 5
at
1 3
2 4
company 1 6
doing
2 6
3 8
engineer
1 2
3 3, 7
great 1 5
hard 2 7
hospital 2 5
java 3 6
nurse 2 3
or 3 4
registered 2 2
software
1 1
3 2
work
2 10
3 9
job_title java developer 3 1
… … … …
field doc term
desc
1
a
at
company
engineer
great
software
2
a
at
doing
hard
hospital
nurse
registered
work
3
a
doing
engineer
java
or
software
work
job_title 1
Software
Engineer
… … …
Terms-Docs Inverted IndexDocs-Terms Forward IndexDocuments
Source: Trey Grainger,
Khalifeh AlJadda, Mohammed
Korayem, Andries Smith.“The
Semantic Knowledge Graph: A
compact, auto-generated
model for real-time traversal
and ranking of any relationship
within a domain”. DSAA 2016.
Knowledge
Graph

Knowledge
Graph
Set-theory View
Graph View
How the Graph Traversal Works
skill: Java
skill: Scala
skill:
Hibernate
skill:
Oncology
doc 1
doc 2
doc 3
doc 4
doc 5
doc 6
skill:
Java
skill: Java
skill: Scala
skill:
Hibernate
skill:
Oncology
Data Structure View
Java
Scala Hibernate
docs
1, 2, 6
docs
3, 4
Oncology
doc 5

Knowledge
Graph
Multi-level Traversal
Data Structure View
Graph View
doc 1
doc 2
doc 3
doc 4
doc 5
doc 6
skill:
Java
skill: Java
skill: Scala
skill:
Hibernate
skill:
Oncology
doc 1
doc 2
doc 3
doc 4
doc 5
doc 6
job_title:
Software
Engineer
job_title:
Data
Scientist
job_title:
Java
Developer
……
Inverted Index
Lookup
Forward Index
Lookup
Forward Index
Lookup
Inverted Index
Lookup
Java
Java
Developer
Hibernate
Scala
Software
Engineer
Data
Scientist
has_related_job_title

Scoring of Node Relationships (Edge Weights)
Foreground vs. Background Analysis
Every term scored against it’s context. The more
commonly the term appears within it’s foreground
context versus its background context, the more
relevant it is to the specified foreground context.
countFG(x) - totalDocsFG * probBG(x)
z = --------------------------------------------------------
sqrt(totalDocsFG * probBG(x) * (1 - probBG(x)))
{ "type":"keywords”, "values":[
{ "value":"hive", "relatedness":0.9773, "popularity":369 },
{ "value":"java", "relatedness":0.9236, "popularity":15653 },
{ "value":".net", "relatedness":0.5294, "popularity":17683 },
{ "value":"bee", "relatedness":0.0, "popularity":0 },
{ "value":"teacher", "relatedness":-0.2380, "popularity":9923 },
{ "value":"registered nurse", "relatedness": -0.3802 "popularity":27089 } ] }
We are essentially boosting terms which are more related to some known feature
(and ignoring terms which are equally likely to appear in the background corpus)
+
-
Foreground Query:
"Hadoop"
Knowledge
Graph

Knowledge
Graph
Multi-level Graph Traversal with Scores
software engineer*
(materialized node)
Java
C#
.NET
.NET
Developer
Java
Developer
Hibernate
ScalaVB.NET
Software
Engineer
Data
Scientist
Skill
Nodes
has_related_skillStarting
Node
Skill
Nodes
has_related_skill Job Title
Nodes
0.90
0.88 0.93
0.93
0.34
0.74
0.91
0.89
0.74
0.89
0.780.72
0.48
0.93
0.76
0.83
0.80
0.64
0.61
0.780.55

Knowledge
Graph
Use Case: Summarizing Document Intent /
Content-based Recommendations
Experiment: Pass in raw text
(extracting phrases as needed), and
rank their similarity to the documents
using the SKG.
Additionally, can traverse the graph
to “related” entities/keyword phrases
NOT found in the original document
Applications: Content-based and
multi-modal recommendations
(no cold-start problem), data cleansing
prior to clustering or other ML methods,
semantic search / similarity scoring

Use Case: Document Classification & Enrichment Knowledge
Graph

Knowledge
Graph
Use Case: Predictive Analytics

Knowledge
Graph
Use Case: Data Cleansing
{ "type":"keywords”, "values":[
{ "value":"hive", "relatedness": 0.9765, "popularity":369 },
{ "value":”spark", "relatedness": 0.9634, "popularity":15653 },
{ "value":".net", "relatedness": 0.5417, "popularity":17683 },
{ "value":"bogus_word", "relatedness": 0.0, "popularity":0 },
{ "value":"teaching", "relatedness": -0.1510, "popularity":9923 },
{ "value":"CPR", "relatedness": -0.4012, "popularity":27089 } ] }
Foreground Query: "Hadoop"
Experiment: Data analyst
manually annotated 500
pairs of terms found together
in real query logs as
“relevant” or “not relevant”
Results: SKG removed 78%
of the terms while maintaining
a 95% accuracy at removing
the correct noisy pairs from
the input data.

Knowledge
Graph
Use Case: Query Expansion
Experiment: Take an initial query, and expand keyword
phrases to include the most related entities to that query
Example:

Use Case: Query Expansion
User’s Query:
machine learning research and development Portland, OR software
engineer AND hadoop, java
Traditional Query Parsing:
(machine AND learning AND research AND development AND portland)
OR (software AND engineer AND hadoop AND java)
Semantic Query Parsing:
"machine learning" AND "research and development" AND "Portland, OR"
AND "software engineer" AND hadoop AND java
Semantically Expanded Query:
("machine learning"^10 OR "data scientist" OR "data mining" OR "artificial intelligence")
AND ("research and development"^10 OR "r&d") AND
AND ("Portland, OR"^10 OR "Portland, Oregon" OR {!geofilt pt=45.512,-122.676 d=50 sfield=geo})
AND ("software engineer"^10 OR "software developer")
AND (hadoop^10 OR "big data" OR hbase OR hive) AND (java^10 OR j2ee)

Example Query Logs
Example Related Keywords (representing multiple meanings)
driver truck driver, linux, windows, courier, embedded, cdl,
delivery
architect autocad drafter, designer, enterprise architect, java
architect, designer, architectural designer, data architect,
oracle, java, architectural drafter, autocad, drafter, cad,
engineer
… …
Source: M. Korayem, C. Ortiz, K. AlJadda, T. Grainger. "Query Sense Disambiguation Leveraging Large Scale User Behavioral Data". IEEE Big Data 2015.

A few techniques:
1) Query Log Mining
2) Semantic Knowledge Graph
Knowledge Graph

Query Log Mining: Discovering ambiguous phrases
(Behavior-based)
1) Classify users who ran each
search in the search logs
(i.e. by the job title
classifications of the jobs to
which they applied)
3) Segment the search term => related search terms list by classification,
to return a separate related terms list per classification
2) Create a probabilistic graphical model of those classifications mapped
to each keyword phrase.

Semantic Knowledge Graph: Discovering ambiguous phrases
(Content-based)
1) Exact same concept, but use
a document classification
field (i.e. category) as the first
level of your graph, and the
related terms as the second
level to which you traverse.
2) Has the benefit that you don’t need query logs to mine, but it will be representative
of your data, as opposed to your user’s intent, so the quality depends on how clean
and representative your documents are.
Additional Benefit: Multi-dimensional disambiguation and dynamic materialization of
categories. Effectively an dynamically-materialized probabilistic graphical model

Disambiguated meanings (represented as term vectors)
Example Related Keywords (Disambiguated Meanings)
architect 1: enterprise architect, java architect, data architect, oracle, java, .net
2: architectural designer, architectural drafter, autocad, autocad drafter, designer,
drafter, cad, engineer
driver 1: linux, windows, embedded
2: truck driver, cdl driver, delivery driver, class b driver, cdl, courier
designer 1: design, print, animation, artist, illustrator, creative, graphic artist, graphic,
photoshop, video
2: graphic, web designer, design, web design, graphic design, graphic designer
3: design, drafter, cad designer, draftsman, autocad, mechanical designer, proe,
structural designer, revit
… …

Using the disambiguated meanings
In a situation where a user searches for an ambiguous phrase, what information can we
use to pick the correct underlying meaning?
1. Any pre-existing knowledge about the user:
• User is a software engineer
• User has previously run searches for “c++” and “linux”
2. Context within the query:
User searched for windows AND driver vs. courier OR driver
3. If all else fails (and there is no context), use the most commonly occurring meaning.
driver 1: linux, windows, embedded
2: truck driver, cdl driver, delivery driver, class b driver, cdl, courier

Who’s in Love with Jean Grey?

Build a Co-occurrence Matrix
http://localhost:8983/solr/job-postings/skg

Related term vector (for query concept expansion)
http://localhost:8983/solr/stack-exchange-health/skg

Score keywords from within a document
http://localhost:8983/solr/job-postings/skg

Contact Info
Trey Grainger
trey.grainger@lucidworks.com
@treygrainger
http://solrinaction.com
Other presentations:
http://www.treygrainger.com

Additional References:
Southern Data Science

The Intent Algorithms of Search & Recommendation Engines

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