tokyogo -- capstone project of galvanize dsi

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