def nearest_ratings(user_id, count, address): """Get Nearest Recommended business to user's address """ logger.debug("User %s rating requested for nearest address", user_id, address) ratings = recommendationengine.get_nearest_businesses( user_id, count, address) return json.dumps(ratings)
def add_ratings(user_id): # get the ratings from the Flask POST request object http://<SERVER_IP>:5432/0/ratings/top/10 ratings_list = request.form.keys()[0].strip().split("\n") ratings_list = map(lambda x: x.split(","), ratings_list) # create a list with the format required by the negine (user_id, movie_id, rating) ratings = map(lambda x: (user_id, int(x[0]), float(x[1])), ratings_list) # add them to the model using then engine API recommendationengine.add_ratings(ratings) return json.dumps(ratings)
def data(self): s = extrapolate_statistics(logging.statistics) cherrypy.response.headers['Content-Type'] = 'application/json' return json.dumps(s, sort_keys=True, indent=4)
def get_user_Ids(): logger.debug("Users") userIds = recommendationengine.get_User_Ids() return json.dumps(userIds)
def business_ratings(user_id, business_id): logger.debug("User %s rating requested for business %s", user_id, business_id) ratings = recommendationengine.get_ratings_for_business_ids( user_id, [business_id]) return json.dumps(ratings)
def ratings_within_category(user_id, count, category): """Get Top Recommendations within the category""" logger.debug("User %s rating requested for category %s", user_id, category) ratings = recommendationengine.get_business_in_categories( user_id, count, category) return json.dumps(ratings)
def ratings_within_state(user_id, count, state): """Get Top Recommendations within the state """ logger.debug("User %s rating requested for state %s", user_id, state) ratings = recommendationengine.get_business_in_state(user_id, count, state) data = json.dumps(ratings) return data
def top_recommendation(user_id, count): """Get Top Recommendations """ top_ratings = recommendationengine.get_top_ratings_with_business_info( user_id, count) return json.dumps(top_ratings)