Restaurant & consumer data

Donated on 8/3/2012

The dataset was obtained from a recommender system prototype. The task was to generate a top-n list of restaurants according to the consumer preferences.

Dataset Characteristics

Multivariate

Subject Area

Computer Science

Associated Tasks

-

Feature Type

-

# Instances

138

# Features

-

Dataset Information

Additional Information

Two approaches were tested: a collaborative filter technique and a contextual approach. (i) The collaborative filter technique used only one file i.e., rating_final.csv that comprises the user, item and rating attributes. (ii) The contextual approach generated the recommendations using the remaining eight data files.

Has Missing Values?

Yes

Variables Table

Variable NameRoleTypeDescriptionUnitsMissing Values
no
no
no
no
no
no
no
no
no
no

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Additional Variable Information

Files, instances and attributes Number of Files: 9 Restaurants 1 chefmozaccepts.csv 2 chefmozcuisine.csv 3 chefmozhours4.csv 4 chefmozparking.csv 5 geoplaces2.csv Consumers 6 usercuisine.csv 7 userpayment.csv 8 userprofile.csv User-Item-Rating 9 rating_final.csv %--- Description format File name Number of instances Number of attributes attribute: Type, Number of missing values (if any), Number of values [list of values] %--- 1 chefmozaccepts.csv Instances: 1314 Attributes: 2 placeID: Nominal Rpayment: Nominal, 12 [cash,VISA,MasterCard-Eurocard,American_Express,bank_debit_cards,checks,Discover,Carte_Blanche,Diners_Club,Visa,Japan_Credit_Bureau,gift_certificates] 2 chefmozcuisine.csv Instances: 916 Attributes: 2 placeID: Nominal Rcuisine: Nominal, 59 [Afghan,African,American,Armenian,Asian,Bagels,Bakery,Bar,Bar_Pub_Brewery,Barbecue,Brazilian,Breakfast-Brunch,Burgers,Cafe-Coffee_Shop, Cafeteria,California,Caribbean,Chinese,Contemporary,Continental-European,Deli-Sandwiches,Dessert-Ice_Cream,Diner,Dutch-Belgian,Eastern_European,Ethiopian,Family,Fast_Food,Fine_Dining,French,,Game,German,Greek,Hot_Dogs, International,Italian,Japanese,Juice,Korean,Latin_American,Mediterranean,Mexican,Mongolian,Organic-Healthy,Persian, Pizzeria,Polish,Regional,Seafood,Soup,Southern,Southwestern,Spanish,Steaks,Sushi,Thai,Turkish,Vegetarian,Vietnamese] 3 chefmozhours4.csv Instances: 2339 Attributes: 3 placeID: Nominal hours: Nominal, Range:00:00-23:30 days:Nominal, 7 [Mon;Tue;Wed;Thu;Fri;Sat;Sun] 4 chefmozparking.csv Instances: 702 Attributes: 2 placeID: Nominal parking_lot:Nominal, 7[public,none,yes,valet_parking,free,street,validated_parking] 5 geoplaces2.csv Instances: 130 Attributes: 21 placeID: Nominal latitude: Numeric longitude: Numeric the_geom_meter: Nominal (Geospatial) name: Nominal address: Nominal,Missing: 27 city: Nominal, Missing: 18 state: Nominal, Missing: 18 country: Nominal, Missing: 28 fax: Numeric, Missing: 130 zip: Nominal,Missing: 74 alcohol: Nominal, Values: 3 [No_Alcohol_Served,Wine_Beer,Full_Bar] smoking_area: Nominal, 5 [none,only_at_bar,permitted,section,not_permitted] dress_code: Nominal, 3 [informal,casual,formal] accessibility: Nominal, 3 [no_accessibility,completely,partially] price: Nominal, 3 [medium,low,high] url: Nominal, Missing: 116 Rambience: Nominal, 2 [familiar,quiet] franchise: Nominal, 2 [t,f] area: Nominal, 2 [open,closed] other_services: Nominal, 3 [none,internet,variety] 6 rating_final.csv Instances: 1161 Attributes: 5 userID: Nominal placeID: Nominal rating: Numeric, 3 [0,1,2] food_rating: Numeric, 3 [0,1,2] service_rating: Numeric, 3 [0,1,2] 7 usercuisine.csv Instances: 330 Attributes: 2 userID: Nominal Rcuisine: Nominal, 103 [Afghan,African,American,Armenian,Asian,Australian,Austrian,Bagels,Bakery,Bar,Bar_Pub_Brewery,Barbecue,Basque,Brazilian,Breakfast-Brunch,British,Burgers,Burmese,Cafe-Coffee_Shop,Cafeteria,Cajun-Creole,California,Cambodian,Canadian,Caribbean,Chilean,Chinese,Contemporary,Continental-European,Cuban,Deli-Sandwiches,Dessert-Ice_Cream,Dim_Sum,Diner,Doughnuts,Dutch-Belgian,Eastern_European,Eclectic,Ethiopian,Family,Fast_Food,Filipino,Fine_Dining,French,Fusion,Game,German,Greek,Hawaiian,Hot_Dogs,Hungarian,Indian-Pakistani,Indigenous,Indonesian,International,Irish,Israeli,Italian,Jamaican,Japanese,Juice,Korean,Kosher,Latin_American,Lebanese,Malaysian,Mediterranean,Mexican,Middle_Eastern,Mongolian,Moroccan,North_African,Organic-Healthy,Pacific_Northwest,Pacific_Rim,Persian,Peruvian,Pizzeria,Polish,Polynesian,Portuguese,Regional,Romanian,Russian-Ukrainian,Scandinavian,Seafood,Soup,Southeast_Asian,Southern,Southwestern,Spanish,Steaks,Sushi,Swiss,Tapas,Tea_House,Tex-Mex,Thai,Tibetan,Tunisian,Turkish,Vegetarian,Vietnamese] 8 userpayment.csv Instances: 177 Attributes: 2 userID: Nominal Upayment: Nominal, 5 [cash,bank_debit_cards,MasterCard-Eurocard,VISA,American_Express] 9 userprofile Instances: 138 Attributes: 19 userID: Nominal latitude: Numeric longitude: Numeric the_geom_meter: Nominal (Geospatial) smoker: Nominal, Missing: 3, 2 [false,true] drink_level: Nominal, 3 [abstemious,social drinker,casual drinker] dress_preference:Nominal, Missing: 5, 4 [informal,formal,no preference,elegant] ambience: Nominal, Missing: 6, 3 [family,friends,solitary] transport: Nominal, Missing: 7, 3 [on foot,public,car owner] marital_status: Nominal, Missing: 4, 3 [single,married,widow] hijos: Nominal, Missing: 11, 3 [independent,kids,dependent] birth_year: Nominal interest: Nominal, 5 [variety,technology,none,retro,eco-friendly] personality: Nominal, 4 [thrifty-protector,hunter-ostentatious,hard-worker,conformist] religion: Nominal, 5 [none,Catholic,Christian,Mormon,Jewish] activity: Nominal, Missing: 7, 4 [student,professional,unemployed,working-class] color: Nominal, 8 [black,red,blue,green,purple,orange,yellow,white] weight: Numeric budget: Nominal, Missing: 7, 3 [medium,low,high] height: Numeric

Dataset Files

FileSize
chefmozhours4.csv71.6 KB
geoplaces2.csv29.5 KB
chefmozaccepts.csv22.1 KB
rating_final.csv21.6 KB
userprofile.csv20.8 KB

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Install the ucimlrepo package 
pip install ucimlrepo
Import the dataset into your code 
from ucimlrepo import fetch_ucirepo 
  
# fetch dataset 
restaurant_consumer_data = fetch_ucirepo(id=232) 
  
# data (as pandas dataframes) 
X = restaurant_consumer_data.data.features 
y = restaurant_consumer_data.data.targets 
  
# metadata 
print(restaurant_consumer_data.metadata) 
  
# variable information 
print(restaurant_consumer_data.variables)
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Citation
Medelln, R. & Serna, J. (2011). Restaurant & consumer data [Dataset]. UCI Machine Learning Repository. https://doi.org/10.24432/C5DP41.
Style:

Creators

Rafael Medelln

Juan Serna

DOI

License

This dataset is licensed under a Creative Commons Attribution 4.0 International (CC BY 4.0) license.

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