tokyogo -- capstone project of galvanize dsi
TRANSCRIPT
TokyoGoCity Attractions Recommender
Wan-Ru YangDec, 2016
Introduction Data Coll ResultsAnalysis
263103 Records
Discussion
FoursquareAPI Query
Webscraping
AWS S3
Photos
PostgreSQLVenues Mongodb
Users
Tips
Introduction Data Coll ResultsAnalysis
Image Tagging
TensorFlow/ Spark EMR
Feature Extraction
Tipgs
DBScan
NMF
Cluster Model User Interface
Web AppLocationVenue Stats
Introduction Data Coll ResultsAnalysis Discussion
FoursquareAPI Query
Webscraping
AWS S3
Photos
PostgreSQL
Mongodb
Introduction Data Coll ResultsAnalysis Discussion
Tipgs
DBScan
NMF
Recommender Web App
LocationVenues
Tips
Users
Data Storage Features Process
Introduction Data Coll ResultsAnalysis Discussion
Introduction Data Coll ResultsAnalysis Discussion
Topic tag Top words
theme tour Famous shrine, totoro, national park …
Game or outdoor Video game, sunshine, bandit
History Garden, manju, oldtokyo
Culture Shinkansen, coast, market
theme park and shopping Indianajons, waterfall, waterpark
Recommended VenuesUser Input
Introduction Data Coll ResultsAnalysis Discussion
Introduction Data Coll ResultsAnalysis Discussion
Next steps:• Improve the model with user – user similarity • Include the seasonality and full tips data• Train a neural network model to tag the image content
Introduction Data Coll ResultsAnalysis Discussion
• The method applied was able to distinguish (to a certain extent) preferences of different groups (local, visitors from other areas in Japan, and forigner travelers).
• My recommender system product of this project will include only the top 200 venues of each visitor source group (sum up to ~ 500 venues) as an toy example that can be deployed on a small amazon instance. The framework can be extended when more data available, and he business features and A/B testing evaluation can be added.
• The NMF analysis indicates visitors to all the venues tend to mention some food, which also indicates that food is an important element that shared among all city attractions! Restaurant recommender is not the topic of this project, but I am expecting to see interesting patterns among different tourist sources in Tokyo.
https://github.com/WanRuYang
https://zuya.siraya.net
https://www.linkedin.com/in/WanRuYang