Presented at Lucene/Solr Revolution 2014

2. Evolving Search Relevancy
James Strassburg
Senior
Architect
-­‐
Direct
Supply
@jstrassburg

3. Agenda
• An Optimization Problem
• Genetic Algorithm Overview
• Modeling Solr Parameters
• Fitness Function

4. sir can you help me… ????
"iam from indonesia want to build
search engine like a Google and i
want to build the system using
Genetic Algorithm but iam confused
what will i do first.
Thanks before."

5. Search Algorithm Parameters
/select?q=foo&defType=dismax
&qf=name^20+desc^10
&pf=name^10&ps=3&mm=2
&bf=”ord(popularity)^0.05”
and many more

6. Where did those numbers come from?
I made them up…
shhhhhhh.
Then we tweaked them after testing.

7. An Optimization Problem
So, how do we know we have the best set of
numbers? Or even a good set?
We have an optimization problem.

8. Sample Schema
<field name="name" type="text_en"
indexed="true" stored="true" required="true"
multiValued="false" omitNorms="true"/>
<field name="description" type="text_en"
indexed="true" stored="true"
multiValued="false" omitNorms="true"/>

9. Sample Data Set
[{
"name":"Red Lobster",
"description":"We deliver the freshest caught seafood every
day."
},{
"name":"Joe's Crab Shack",
"description":"We serve delicious red crabs, rock crabs,
large lobsters, and other delicious seafood. Our lobsters are
our specialty."}]
http://localhost:8983/solr/restaurantsCollection/select?q=red+lobster&defType=dismax&qf=name
+description&indent=true&fl=name+description

10. Genetic Algorithms
• A tool for solving optimization problems
• Based on ideas from genetics, evolution,
and natural selection
• DEAP – Distributed Evolutionary
Algorithms in Python

11. Genetic Algorithms
• Define candidate solution encoding
• Define a fitness function
• Generate random solutions
• Select candidates for reproduction
• Use crossover and mutation to create a new
generation
• Repeat until some criteria is met

12. Crossover and Mutation
Parent 1: [1,0,1,1,1,0,1,1]
Parent 2: [0,0,0,0,1,1,1,1]
Child: [1,0,0,1,1,0,1,0]

13. Encoding Parameters
>>> sys.float_info
sys.float_info(max=1.7976931348623157e+308,
max_exp=1024, max_10_exp=308,
min=2.2250738585072014e-308, min_exp=-1021,
min_10_exp=-307, dig=15, mant_dig=53,
epsilon=2.220446049250313e-16, radix=2,
rounds=1)

14. Encoding Parameters
>>> import numpy
>>> single = numpy.float32(3.4)
>>> single
3.4000001
>>> half_single = numpy.float16(3.4)
>>> half_single
3.4004

15. Encoding Parameters
/select?q=foo&qf=field^35.2
versus
/select?q=foo&qf=field^35.3

16. Decimal / Fibonacci Encoding
• 0, 0.2, 0.4, 0.8, 1, 2, 3, 5,
8, 13, 21, 34, 55, 89, 144
• 16 values encode into 4-bits
• Supports fast evolution
• Avoids relative maxima

17. Decimal / Fibonacci Encoding
0.0 => [0, 0, 0, 0]
0.2 => [0, 0, 0, 1]
0.4 => [0, 0, 1, 0]
…
1 => [0, 1, 0, 1]
2 => [0, 1, 1, 0]
…
144 => [1, 1, 1, 1]

18. Candidate Solution Encoding
/select?q=foo&qf=name^0.4+desc^13
0.4 => [0, 0, 1, 0]
13 => [1, 0, 1, 0]
Candidate Solution: [0, 0, 1, 0, 1, 0, 1, 0]

19. Fitness Function
• Measure how well a candidate solution
solves the problem
• Should be very fast

20. Normalized Discounted Cumulative Gain
• Very relevant > relevant > not relevant
• Relevant results are more useful if they
appear earlier
• Results should be irrelevant of the query

22. Precision and Recall
Precision – Likelihood that a returned result was
correct
Recall – Likelihood that a relevant result was
returned

23. F-measure
• Harmonic mean of precision and recall
• Punishes outliers

24. Analytics in Schema
<field name="searchTermInteractions"
type="lowercase" indexed="true"
stored="true" multiValued="true"/>

25. Demo

26. Resources
• DEAP - https://code.google.com/p/deap/
• My github repo for this example -
https://github.com/jstrassburg/evolving-search-relevancy
• @jstrassburg