load_yelp_dataset.py

import graphlab as gl

# Data available to download from 
# https://www.yelp.com/academic_dataset
business = gl.SFrame.read_csv('yelp_academic_dataset_business.json', 
                header=False, delimiter='\n', column_type_hints=dict)
checkin = gl.SFrame.read_csv('yelp_academic_dataset_checkin.json', 
                header=False, delimiter='\n', column_type_hints=dict)
review = gl.SFrame.read_csv('yelp_academic_dataset_review.json', 
                header=False, delimiter='\n', column_type_hints=dict)
user = gl.SFrame.read_csv('yelp_academic_dataset_user.json', 
                header=False, delimiter='\n', column_type_hints=dict)
tip = gl.SFrame.read_csv('yelp_academic_dataset_tip.json', 
                header=False, delimiter='\n', column_type_hints=dict)

# Changing JSON into tables, i.e. SFrames
reviews = review.unpack('X1', column_name_prefix='')
businesses = business.unpack('X1', column_name_prefix='', 
              limit=['business_id', 'name', 'latitude', 'longitude', 'stars'])

# Build a recommender system
m = gl.recommender.create(reviews, 'user_id', 'business_id')

# Find businesses that are similar based on users in common
similar_items = m.get_similar_items(['BVxlrYWgmi-8TPGMe6CTpg'])
print similar_items.join(businesses, on={'similar_item':'business_id'})