Data Science Practice

1. Let AI plan your trip
Oleksandra Kardash for AI & Big Data Day

2. Task
Problem: Trip planning takes long time before travel or costs some money in case of
involvement of third parties.
Idea: develop personalized trip recommender that will suit user interests and take
into account objective factors.

3. Data sources
Places data User data External data
 Google  Q&A  10 times
 Foursquare  User History  Weather underground
 Yelp  Facebook  Holiday Calendar
 OpenTable  Instagram  timeanddate.com
 TripAdvisor  User reviews  Public datasets
 CNTravel
 Open Street Map
 Commercial databases
Cold start problem
GDPR

4. Data sources
Foursquare
 Places API
 Places database
 Pilgrim SDK
Open Street Map
 Lots of APIs
 Places info
 Duration and distance
Facebook
 Graph API
 require user approve
 examples of the use of this
information

5. Minimum input features
Places data User data External data
 Geo coordinates  Dates  Weather conditions
 Category  Categories preferences  Seasonality
 Open hours  Places user like/dislike  Traffic
 Adaptive rating  Events
 Sentiment coefficient  Holidays

6. Places quality
Problems:
 Rating varies from source to source
 Rating absence in some sources
 Rating from some sources is not relevant because of small reviews number
Solutions: develop own system for measuring places quality based on existing data
Source 1 Source 2 Source 3 Source 4
Rating Count Sentiment
stats
Rating Count Sentiment
stats
Rating Count Sentiment
stats
Rating Count Sentiment
stats
Place 1 4.2 6690 3 10 null 0 0 3.5 1700
Place 2 4.2 129 3.5 3 4.8 40 null 0 0
Place 3 5 5 3.5 7 null 0 0 4 5

7. Sentiment analyzer
Sentiment analysis is extracting, identifying, or otherwise characterizing the sentiment content of a
text unit using NLP, statistics, or machine learning methods.
Amazon reviews public dataset:
 Built model for predicting sentiment score
 Problem: dataset is not fully applicable to our domain
Google Cloud Natural Language service for extending

8. Google Cloud Natural Language
Feature 0 - 5K 5K+ - 1M 1M+ - 5M 5M+ - 20M
Entity Analysis Free $1.00 $0.50 $0.25
Sentiment Analysis Free $1.00 $0.50 $0.25
Syntax Analysis Free $0.50 $0.25 $0.125
Entity Sentiment
Analysis
Free $2.00 $1.00 $0.50
Feature 0 - 30K 30K+ - 250K 250K+ - 5M 5M+
Content Classification Free $2.00 $0.50 $0.10

9. Google Cloud Natural Language

10. Places quality

11. User profile
 preferred categories (Q&A)
 Liked/disliked places
Users like/dislike matrix is very sparse

12. Places similarity
Problem: How to understand which places user will like by
places that user have already liked?
Solution: Places similarity based on their description and
reviews
How? Train doc2vec model
Challenge: Multicategories similarity
A framework for learning paragraph vector
t-SNE (t-distributed Stochastic Neighbor Embedding)

13. Places similarity
Hans Christian Andersen Statue
Memorial monument
Hans Christian Andersen statue was erected in 1956 to commemorate the author's 150th birthday. This
tribute to the Danish poet, novelist, and children's author was made possible because of a large donation
by the Danish American Women's Association. The large, bronze statue depicts Andersen seated upon a
granite bench, reading from his book The Ugly Duckling. Sculpted by Georg John Lober, this children's
statue is meant to be climbed on and is a popular attraction for kids.
Scandinavia House
Museum
Inside this lovely, airy space—a fitting homage to the natural simplicity of Scandinavian design—you can
walk around the gallery of Scandinavian art, come see classic and cutting-edge movies, live concerts,
readings and lectures that celebrate the history and culture of the region, or sign up for a language class
in Danish, Norwegian or Swedish; some events are by paid admission. If you find yourself wandering
around Midtown, pop in for a light bite at the cheery café and check out the center’s shop for stunning
textiles, jewelry, tableware and decorative objects.

14. Place Recommender
What is recommender system?
RS is computer-based tool, which attempt to predict items out of large pool a user may highly
likely be interested in, and to suggest him the best one
Based on:
 Past user behavior
 Relations to other users
 Item similarity
 Context

15. Place Recommender
Types:
 Collaborative Filtering Systems – aggregation of consumers’
preferences and recommendations to other users based on
similarity in behavior patterns
 Content-based Systems are based on a description of the item
and a profile of the user’s preferences.
 Context-aware approaches use the context in its calculation to
predict items likely to interest the user
 Hybrid
Tell me what's popular among my peers
Show me more of the same I’ve liked
Tell me what fits based on my
needs in certain situation

16. One day – one geo cluster
What we have:
 Recommended places
 N - Number of days (from user Q&A)
What we can do:
Clustering recommended places to N days
As result we will have personalized places for
each day of user trip
Further more:
Not enough places for day? Lets call other
places which are located no further than
certain radius from clusters centroids.

17. Sum up

18. Oleksandra Kardash
Email: Oleksandra.Kardash@eleks.com
Skype: live:ashassik