data day seattle duplicate detection via topic modeling
TRANSCRIPT
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
Over 2 million global HA + Comp documents and meta data
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.
Hypothesis
We can detect properties listed on HomeAway and the competition by comparing the text in the property descriptions
Worked great, but...
“Large” Vocabulary size
~10K Tokens -> 10K 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?
Hypothesis
We can detect properties listed on HomeAway and the competition by comparing the text in the property descriptions
We can leverage Topic Modeling to do it
Latent Dirichlet Allocation (Topic Modeling)
Communications of the ACM, Vol. 55 No. 4, Pages 77-8410.1145/2133806.2133826
Topic Modeling and LDA
In natural language processing, Latent Dirichlet allocation (LDA) is a generative model that allows sets of observations to be explained by unobserved groups that explain why some parts of the data are similar.
(Wikipedia)
Cat, Dog, Fish, Turtle,
Hamster
Cat, Dog, Mass,
Hysteria, Living,
Together
Cat, Dog, Cold, Rain,
Hot, Temperature
Document A
Document B
Document C
Some Example Topics from Breckenridge
time, setting, wifi, elk, central, enjoying, spend, marijuana, sleepers, brittany
buffalo, soaking, pubs, titles, washroom, pristine, ratedgas, multiple, especially, scrumptious
apartment, weekend, maintained, company, bedroom, bed, sized, bathroom, walk, queen
golf, course, chateau, sole, beauty, payment, splendor, championship, rooftop, stonehaven
smoking, allowed, deposit, damage, fee, owner, dates, paid, balance, zone
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
Create an experimental dataset
Original Corpus
Create an experimental dataset
Original Corpus
Random selection
Create an experimental dataset
Original Corpus
Random selection
Duplicate (with optional degradation)...… and see if we can find those duplicates
How to make something useful?
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
● But what about Locality Sensitive Hashing?
Current Status
Questions?
Brent SchneemanPrincipal Data Scientist
HomeAway, Inc.
@schnee
← https://www.homeaway.com/vacation-rental/p3482065