Austin Texas NLP Community Day chat. Using Topic Modeling to find duplicate listings in HomeAway's data

1. Duplicate Detection via
Topic Modeling

2. HomeAway Key Facts
● 1,300,000+ global vacation rental listings
● 200,000,000+ vacation days / year
● ~190 countries, 22 languages
● HQ in Austin, TX; part of Expedia, Inc
--> Capable competition and fraud vectors

3. Competitive Intelligence

4. Breckenridge Colorado
HomeAway in blue

5. Breckenridge, zoomed in

6. Same Property

7. The Property Descriptions
Why Property Descriptions?
● Almost identical text
● Similar descriptions
seemed probable
○ Consistent owner
branding, easy to
replicate
● Tech team wanted to use
natural language
processing techniques
● Didn’t know if this would
work when we began
The Other Guys
There are truly inspiring views at High Point Retreat and
plenty of places to sit and enjoy them. Take a load off in one
of the many rooms with views of the ski mountain and
remember how lucky you are to live like this. Cozy up with
family in the sunken living room and chat for hours on end.
Sit in a circle of tree stumps around the outdoor fire pit and
roast marshmallows. After all that sitting, youll be more than
happy to walk 250 yards to the free shuttle to get the blood
pumping again. Then, have a seat and enjoy your free ride.
Best. Vacation. Ever. Vacation homes allow families to
stay...together. At InvitedHome, we think that's pretty
important, so we do everything in our power to make your
vacation totally epic. Not only do we choose the best homes
in the best destinations, but we make the experience
effortless so you can really enjoy yourself. Our team will
stock your fridge, babysit the kids, cater your party, plan your
day trip, make reservations, and do whatever we can to
make sure you have the Best. Vacation. Ever.
HomeAway
There are truly inspiring views at High Point Retreat and
plenty of places to sit and enjoy them. Take a load off in one
of the many rooms with views of the ski mountain and
remember how lucky you are to live like this. Cozy up with
family in the sunken living room and chat for hours on end.
Sit in a circle of tree stumps around the outdoor fire pit and
roast marshmallows. After all that sitting, you’ll be more
than happy to walk 250 yards to the free shuttle to get the
blood pumping again. Then, have a seat and enjoy your free
ride.
Best.Vacation.Ever. Vacation homes allow families to stay...
together. At InvitedHome, we think that's pretty important,
so we do everything in our power to make your vacation
totally epic. Not only do we choose the best homes in the
best destinations, but we make the experience effortless so
you can really enjoy yourself. Let us connect you with the
best options in town for babysitting, equipment rental,
transportation, catering, day trips, shopping, dining, and
even stocking your fridge with groceries! We’ll do everything
in our power to make sure you have the Best. Vacation.
Ever.

8. Worked great, but...
“Large” Vocabulary size
~6300 Tokens -> 6300 Dimensions and
millions of sparse vectors
A little slow
(took a week to process the US)
Initial Approach: TF-IDF and Cosine Distance

9. Spark
Clusters?
Topic
Modeling?
Other Distance
Metrics?

10. Latent Dirichlet Allocation (Topic Modeling)
Communications of the ACM, Vol. 55 No. 4, Pages
77-84
10.1145/2133806.2133826

11. Topic Modeling Motivations
● Smaller dimensional space
● Faster processing times
● At the end, we’d have Topic Models
Must be useful for duplicate detection
We used Spark’s ML APIs for this:
val countLDA = new LDA()
.setK(numTopics)
.setMaxIter(params.maxIterations)
.setSeed(params.randomSeed)
.setFeaturesCol(featureCol)
.setTopicDistributionCol("topicDistribution")

13. Distances between Topic Distributions
Euclidean Manhattan Cosine

14. Distances between Topic Distributions
Euclidean Manhattan Cosine
Jensen-Shannon Hellinger

15. Distances between Topic Distributions
Euclidean Manhattan Cosine
Jensen-Shannon Hellinger

19. How to make something useful?
This is a machine learning effort

22. Interquartile Ranges are more resilient to outliers than
standard deviations
IQRs bring information about the entire set of possible
duplicates
Random Forest Model (R):
trainIdx <- createDataPartition(dupesFoundByTopic$match,
p=0.9, list=FALSE, times=1)
train <- dupesFoundByTopic[trainIdx,]
fit <- randomForest(as.factor(match) ~ distance + iqrs,
data=train)
Combining Distance and IQR
Feature Mean
Decrease
Gini
distance 498
IQR 57
Reference
Pred. FALSE TRUE
FALSE 204 2
TRUE 4 32

23. ● Topic Models / Topic Distances seem useful
○ Esp. when part of a multi-signal model
(i.e. images)
● Hybrid Spark and R approach
○ Moving to 100% Spark in future for
speed
● Topic Models just sitting there, waiting for
exploitation
○ “Programmatic” Marketing Efforts, &c.
Current Status

24. Questions?
Brent Schneeman
Principal Data Scientist
HomeAway, Inc.
brent@homeaway.com
careers.homeaway.com
@schnee
← https://www.homeaway.com/vacation-rental/p3482065