The document outlines Booking.com's innovative approach to recommending travel destinations by leveraging contextual user profiles and data from endorsements, achieving increased user engagement rates. It discusses the limitations of traditional recommending systems and presents a multi-criteria recommendation framework that addresses the cold start problem by incorporating various contextual features. The results show a 22.5% improvement in click-through rates while using contextual profiles for personalized recommendations.

1. Booking.com
Recommend Now,
Not in the Past
Lucas Bernardi, Melanie Mueller
Data scientists @ Booking.com
Leveraging Contextual User Profiles
for Destination Recommendations

2. Booking.com
Introduction: Recommending travel destinations
Part I: Ranking destinations
Part II: Contextual recommendations
Conclusions
Outline

3. Booking.com
Travel agents

4. Booking.com
Online travel agents

5. Booking.com
Destination Finder

6. Booking.com
Destination Finder

7. Booking.com
Destination Finder

8. Booking.com
Which destinations to recommend?
Destination recommendations
1) Match activities
2) Recommend best matching destinations

9. Booking.com
• 5 million reviews
Endorsement data
• 256 activities mined from reviews (LDA)
• Ask users to ‘endorse’ a destination after their stay,
e.g. ‘Beaches’, ‘Temples’
Endorsement #given #destinations
Shopping 876,726 11,708
Food 525,111 20,538
Beach 505,192 11,422
… … …
Mythology 1,065 406
Heliskiing 354 165

10. Booking.com
Endorsement data

11. Booking.com
Endorsement data
• Standard recommender: user gives rating for item
• Here: multi-criteria, negative opinions are hidden

12. Booking.com
Ranking for ‘beach’
• Naive Bayes
P(Miami | beach) =
(# beach endorsements for Miami)
(# beach endorsements)
• Keep it simple!

13. Booking.com
Destination Finder

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Evaluation
• Naive Bayes
• Random
• Popularity
How to test?

15. Booking.com
A/B testing
• Version A • Version B • Version C

16. Booking.com
A/B testing
Ranker #users
Random 10079
Popularity 9838
Naive Bayes 9895
• Which metric?
User engagement → clicks

17. Booking.com
A/B testing
Ranker #users #clickers conversion g-test
Random 10079 2465 24.46%
Popularity 9838 2509 25.50% 90.8%
Naive Bayes 9895 2645 26.73% 99.9%
• Which metric?
User engagement → clicks

18. Booking.com
Introduction: Recommending travel destinations
Part I: Ranking destinations
Part II: Contextual recommendations
Conclusions
Outline

19. Booking.com
History is history
• Traditional recommending systems use past user ratings to
predict unknown ratings
• User History is short: User Cold start problem.
• Users have different personas: History becomes less
relevant
• User Interests are volatile: History becomes less relevant
Continuous User Cold start

20. Booking.com
Context
Definition
Set of features that inform about the current situation of the user.
Example
Location, device, weather conditions, season, day of the week,
hour of the day.
Hypothesis
Users in similar contexts
have similar interests.

21. Booking.com
Context
Traditional Collaborative Filtering Recommendations
U x I → R
Destination Finder
U x I x C → <r0
, r1
, r2
, …, rn
>

22. Booking.com
Framework

23. Booking.com
Framework

24. Booking.com
Discovering contextual profiles

Data points: Reviews

Features:

Endorsements

Contextual features

All features one-hot-encoded

Example:
<Ubuntu, Firefox, Tuesday, beach, temples>
<0,1,0,0,0,1,0,0,1,0,0,0,0,0,0,1,0...,0,1,0...0>

25. Booking.com
Discovering contextual profiles

k-means Clustering

Clean up clusters

Final output

Q binary n-dimensional centroids

Q Contextual Profiles

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Discovering contextual profiles
Contextual Profiles
i - 1 i i + 1 i + 1
… iPhone.OS.7.Chrome Windows.Phone …
iPhone.OS.5.Chrome Windows.Vista
iPhone.OS.6.Chrome Friday
Android.2.2 Sunday
Android.2.2.Tablet
Android.3.1.Tablet
Android.4.0.Tablet
Android.4.4.Tablet
Android.2.1.Tablet
Android.3.0.Tablet
Android.4.1
Android.4.3.Tablet

27. Booking.com
Applying contextual profiles

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Applying contextual profiles

Contextual Profiles are simply a binary vector

Find the most similar Contextual Profile for each review

Train a ranker for each Contextual Profile

29. Booking.com
Computing recommendations

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Computing recommendations
 For a given user, compute a feature vector using contextual
features
 Contextualize: Find closest Contextual Profile
 Compute recommendations using the ranker trained on the
selected Contextual Profile

31. Booking.com
Framework

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Results
Ranker Users Conversion CTR
Baseline 13,306 21.7 ± 0.7% 18.5 ± 0.4%
Contextual 13,562 21.3 ± 0.7% 22.2 ± 0.4%
Improved CTR by 22.5%

33. Booking.com
Conclusions
• Multi-criteria destinations Recommender System
• Simple rankers increase user engagement
• Context matters
•Improves user engagement
•Attacks Continuous cold start problem
• Reusable Contextual Profiles
• On-line evaluation

34. Booking.com
Julia Kiseleva,
PhD student at Eindhoven University,
Research intern at Booking.com
Nov 204 - Mar 2015
Booking.com:
Chad Davis, Ivan Kovacek
Mats Stafseng Einarsen
Academia:
Djoerd Hiemstra, Jaap Kamps
Mykola Pechenizkiy, Alexander Tuzhilin
Thanks

35. Booking.com
We're hiring