This document discusses optimizing travel destination recommendations based on user preferences using data from Booking.com. It describes Booking.com's data on destinations, endorsements from past users, and challenges in ranking destinations for new users due to sparsity, volatility and cold start problems. An A/B test is run comparing different ranking methods (baseline, random, popularity, naive Bayes) to maximize user engagement, finding that a simple naive Bayes approach outperforms others. Future work to improve rankings by considering user context is discussed.
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Where to Go on Your Next Trip? Optimizing Travel Destinations Based on User Preferences
1. Where to Go on Your Next Trip?
Optimizing Travel Destination Based on User
Preferences
Julia Kiseleva, Melanie J.I. Mueller, Lucas Bernardi,
Chad Davis, Ivan Kovacek, Mats Stafseng Einarsen, Jaap
Kamps, Alexander Tuzhilin, Djoerd Hiemstra
SIGIR 2015,
Chile
7. How Do We Get Endorsements?
• Only users who stayed at a hotel in a destination can
endorse it
• Free text endorsements since 2013
• Since 2014 free text endorsements standardized to 256
canonical tags
• Used NLP techniques to extract the canonical base
More Numbers:
• > 13.000.000 total unique endorsements
• > 60.000 destinations
14. Problem Setup
• Challenge:
o Recommender or Information Retrieval System?
• Information Retrieval:
o Users have information need which is expressed as a query
o System has to satisfy this information needs
• Recommender:
o System predicts what users might be interested
We have both:
1) Users search for activities
2) Users don’t know where they want to go
15. Why is It Hard?
Problem Characteristics:
• S - Sparsity: new or rare users/destinations
• V - Volatility: users’ interests/endorsements of destinations change over
time
• I - Identity: a failure to match data from the same users
• P - Personas: users have different interests at different, possibly closely
points in time
Continuous Cold Start Problem!
16. Ranking Destinations for ‘Beach’
• Keep it simple!!! We care about performance!
• Naïve Bayes:
P(Miami, Beach) = P(Miami) * P(Miami | Beach)
P(Miami | Beach) = # ‘Beach’ endorsements for Miami
# ‘Beach’ endorsements
17. What and How to Compare?
• Booking.com Baseline
• Random
• Most Popular Destination
• Naïve Bayes
Objection:
Increase User Engagement
(Clickers per SERP)
18. A/B Testing Setup
• 50/50 traffic split
• Experiments run for N full weeks according to desired
power and significance levels
• Hypothesis tests are performed according to targeted
metrics (G-test in our case)
23. Conclusion and Future Work
• Interesting application
• Surprise 1: Keep simple baseline in production system
• Surprise 2: Random performed not bad => effect
serendipity
For the Future:
• Improve the ranking by taking contexts into account
24.
25. Conclusion and Future Work
• Interesting application
• Surprise 1: Keep simple baseline in production system
• Surprise 2: Random performed not bad => effect
serendipity
For the Future:
• Improve the ranking by taking contexts into account
Editor's Notes
Put numbers
Start from IR problem = nothing new
Users don’t know what they want => serendipity is very important +> formulate it as Recommendation Problem
Think!!!!