Duplicate Detection via Topic Modeling

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

Competitive Intelligence

Breckenridge Colorado

HomeAway in blue

Breckenridge, zoomed in

Same Property

The Property DescriptionsWhy 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 GuysThere 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.

HomeAwayThere 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.

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

Spark Clusters?

Topic Modeling?

Other Distance Metrics?

Latent Dirichlet Allocation (Topic Modeling)

Communications of the ACM, Vol. 55 No. 4, Pages 77-8410.1145/2133806.2133826

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")

Distances between Topic Distributions

Euclidean Manhattan Cosine

Distances between Topic Distributions

Euclidean Manhattan Cosine

Jensen-Shannon Hellinger

Distances between Topic Distributions

Euclidean Manhattan Cosine

Jensen-Shannon Hellinger

How to make something useful?

This is a machine learning effort

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

● 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

Questions?

Brent SchneemanPrincipal Data Scientist

HomeAway, Inc.

brent@homeaway.comcareers.homeaway.com

@schnee

← https://www.homeaway.com/vacation-rental/p3482065