4. follow the Hippo trail
Hippo GetTogether 2014
Contingency Table
A not A
B x 20 - x 20
not B 40 - x 140 + x 180
40 160 200
Documents A, B
total # visitors
visitors of B
visitors of A
x
P(x >= 8) ≈ 3%
visitors of A & B
5. follow the Hippo trail
Hippo GetTogether 2014
Co-occurrence Insights
Insight: a high cohesion of page visits in the partner section
standing out from the regular ‘.com’ visitor cluster suggests that
visitors looking for a partner go through every single page and
probably can’t find what they’re looking for.
Action: Hippo suggests to improve navigation, search or filtering.
● attribute / url
relatedness
find partner
/fr
.com.org
genericrelease
notes
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Hippo GetTogether 2014
Recommendations
Alice Bob Charlie
Star Wars 3 4
Finding
Nemo
3 4
Sound of
Music
5 1 2
genre stars
Star Wars sci-fi Portman
Finding
Nemo
animation DeGeneres
Sound of
Music
musical Andrews
user - item (rating)
collaborative filtering
content
(meta) data
which documents are interesting for ME?
find docs similar to visited documents find docs co-occurring with visited documents
7. follow the Hippo trail
Hippo GetTogether 2014
Implementation
combine in search index:
Recommendation Query
Content-based:
(meta) data
Collaborative Filtering:
co-occurrence
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Hippo GetTogether 2014
Recommended For You
1.Collect ID of viewed content
2.Calculate co-occurrences
3.Index, along with content
IDs of co-viewed documents
4.Search with recent IDs, similarity
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Hippo GetTogether 2014
Patterns in the Data
customers that buy diapers often buy beer as well
(young dads rewarding themselves?)
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Itemsets Rules
Find the patterns (association rule mining):
1.sets of items that are bought together
P(beer,diapers) > 1%
(support)
1.subsets that are good predictors
> 4 (lift)P(beer,diapers)
P(beer) P(diapers)
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Hippo GetTogether 2014
http://www.onehippo.com/en/thankyou - Thank You
Beer? Diapers? Conversions!!!
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Hippo GetTogether 2014
http://www.onehippo.com/en/thankyou
will a visitor go there?
P(conversion|request log)
what are the relevant “signals”?
which configuration performs best?
15. follow the Hippo trail
Hippo GetTogether 2014
Patterns For Conversion
single item:
referrer www.google.com
pattern/itemset:
visited demo
2014 week 4
correlations
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Hippo GetTogether 2014
Scary Data
Structure
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Hippo GetTogether 2014
1.Build Frequent Prefix Tree
(FPGrowth)
2.Extract patterns relevant for conversion
(using contingencies)
Finding Frequent Itemsets
18. follow the Hippo trail
Hippo GetTogether 2014
Pattern Contingency Table
converted not converted
pattern
matches
pattern
does not
match
converted
● visited /thankyou
sample pattern
● visited demo
● in 2014 week 4
19. follow the Hippo trail
Hippo GetTogether 2014
Sub-Pattern Filtering
Problem:
when pattern (A, B, C) is relevant, patterns
(A), (B), (C), (A, B), (A, C), (B, C)
(likely) also match. E.g. with C meta-data on page B.
Solution:
test for independence using contingency!
20. follow the Hippo trail
Hippo GetTogether 2014
Actionable Insights?
The found itemsets are quite numerous and
seem to contain a lot of redundancy.
But they are certainly interesting, e.g. for a
periodic evaluation.
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Hippo GetTogether 2014
Personalization
Putting Patterns to Use
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Naive A/B Testing
The naive solution:
route some traffic to alternative configuration
A (old config): 80%
B (new config): 20%
run for some time
see if B has relatively more conversions
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Hippo GetTogether 2014
Problems With Naive Solution
if B is drastically worse,
20% of traffic is LOST
marketer must regularly check and decide
when has a new config PROVEN itself?
number of concurrent experiments is LOW
no user context
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Hippo GetTogether 2014
Predict Conversion
Conversion rate depends on context:
x the patterns
w the “weights”
ϕ cdf of normal dist.
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Experimental Setup
Split data set (.org + .com)
1.training set
189660 visitors, 435 conversions
2.test set
27013 visitors, 40 conversions
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Hippo GetTogether 2014
Can We Predict Conversion?
1260 itemsets
ROC curve
TPR versus FPR
@ false positive rate 10%
: 96% true positive rate
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Hippo GetTogether 2014
Towards Actionable Insights
Use
A utomatic
R elevance
D etermination
to prune the patterns
(optimize the prior)
σ
μ
relevant
irrelevant
weights (w)
29. follow the Hippo trail
Hippo GetTogether 2014
Top 20 Patterns For Conversion
referer.go.onehippo.com
.pathInfo./resources/whitepapers/forrester-market-
overview-web-content-management-systems.html
.pathInfo./resources/whitepapers/cms---a-critical-
solution-for-todays-ecommerce.html
.pathInfo./resources/whitepapers/hippo-cms-for-the-
enterprise.html
.pathInfo./resources/whitepapers/web-content-
management-in-the-cloud.html
.collectorData.channel.One Hippo English Site
.collectorData.audience.terms.
referer.www.onehippo.com
.collectorData.categories.terms.cms
.pathInfo./mobile-cms
.collectorData.channel.One Hippo English Site
.pathInfo./ressourcen/demo
.pathInfo./resources/videos/hippo-cms-grand-
tour.html
.collectorData.channel.One Hippo English Site
.collectorData.audience.terms.
.collectorData.categories.terms.cms
.pathInfo./ressources/demo
.pathInfo./what_to_buy/compare.html
referer.www.cmswire.com
.pathInfo./resources/demo
.collectorData.categories.terms.mobile
.pathInfo./resources/whitepapers/understanding-hippo-cms-7-
software-architecture.html
.pathInfo./resources/whitepapers/selecting-today’s-
enterprise-web-content-management-system.html
.collectorData.channel.One Hippo English Site
referer.www.google.nl
referer.www.onehippo.com .pathInfo./resources/videos/a-
quick-overview-of-hippo-cms-in-just-under-3-minutes.html
.collectorData.categories.terms.repository
.pathInfo./resources/whitepapers/selecting-today’s-
enterprise-web-content-management-system.html
.collectorData.categories.terms.
.collectorData.categories.terms.relevance
30. follow the Hippo trail
Hippo GetTogether 2014
Actionable Insights!
we can find a
small model
that can be used for
human interpretation
and
automated personalization
31. follow the Hippo trail
Hippo GetTogether 2014
Product Challenge
KISS
# parameters should be minimal
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Hippo GetTogether 2014
Parameters
Recommendations
1 hyper-param
Personalization
idem
NICE!