This document describes a city attractions recommender system called TokyoGo that was developed using data collected from Foursquare APIs and web scraping. The system used machine learning techniques like NMF and DBScan clustering to analyze over 263,000 venue records and user tips to generate topic tags and recommendations. Evaluation of the model found it was able to distinguish preferences of local and foreign visitors to some extent. Further improvements and applications of the system are discussed.