Search engines, recommendation systems, advertising networks, and even data analytics tools all share the same end goal - to deliver the most relevant information possible to meet a given information need (usually in real-time). Perfecting these systems requires algorithms which can build a deep understanding of the domains represented by the underlying data, understand the nuanced ways in which words and phrases should be parsed and interpreted within different contexts, score the relationships between arbitrary phrases and concepts, continually learn from users' context and interactions to make the system smarter, and generate custom models of personalized tastes for each user of the system.
In this talk, we'll dive into both the philosophical questions associated with such systems ("how do you accurately represent and interpret the meaning of words?", "How do you prevent filter bubbles?", etc.), as well as look at practical examples of how these systems have been successfully implemented in production systems combining a variety of available commercial and open source components (inverted indexes, entity extraction, similarity scoring and machine-learned ranking, auto-generated knowledge graphs, phrase interpretation and concept expansion, etc.).
SuccessFactors 1H 2024 Release - Sneak-Peek by Deloitte Germany
Intent Algorithms: The Data Science of Smart Information Retrieval Systems
1. Intent Algorithms: The data science of
smart information retrieval systems
Trey Grainger
SVP of Engineering, Lucidworks
Southern Data Science Conference
2017.04.07
2. 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
About Me
20. 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
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23. 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
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24. 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
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25. 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
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26. 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
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31. 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()
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32. 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()
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33. • 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
*Source: Solr in Action, chapter 3
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34. 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.
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35. 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?
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36. Southern Data Science
*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%
40. 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”
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41. ● 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
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42. 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 $60K
*Example from chapter 16 of Solr in Action
Consider what you know about users
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56. Learning to Rank (LTR)
● It applies machine learning techniques to discover the best combination
of features that provide best ranking.
● It requires labeled set of documents with relevancy scores for given set
of queries
● Features used for ranking are usually more computationally expensive
than the ones used for matching
● It typically re-ranks a subset of the matched documents (e.g. top 1000)
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60. #1: Pull, Build, Start Solr
git clone https://github.com/apache/lucene-solr.git && cd lucene-solr/solr
ant server
bin/solr -e techproducts -Dsolr.ltr.enabled=true
#2: Run Searches
http://localhost:8983/solr/techproducts/browse?q=ipod
#3: Supply User Relevancy Judgements
cd contrib/ltr/example/
nano user_queries.txt
#4: Install Training Library
curl -L https://github.com/cjlin1/liblinear/archive/v210.zip > liblinear-2.1.tar.gz
tar -xf liblinear-2.1.tar.gz && mv liblinear-210 liblinear
cd liblinear && make && cd ../
#5: Train and Upload Model
./train_and_upload_demo_model.py -c config.json
#6: Re-run Searches using Machine-learned Ranking Model
http://localhost:8983/solr/techproducts/browse?q=ipod
&rq={!ltr model=exampleModel reRankDocs=25 efi.user_query=$q}
65. Building a Taxonomy of Entities
Many ways to generate this:
• Statistical Analysis of interesting phrases
- Word2Vec / Glove / Dice Conceptual Search
• 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.Southern Data Science
73. 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.
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74. 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.
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78. 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
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79. 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
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
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80. 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
Scoring nodes in the Graph
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": 0.9765, "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"
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81. 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
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
has_related_job_title
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
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85. Knowledge
Graph
Use Case: Summarizing Document Intent
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