Context This dataset was used for a study where the task was to generate a top-n list of restaurants according to the consumer preferences and finding the significant features. 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. Content There are 9 data files and a README, and are grouped like this: 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 More detailed file descriptions can also be found in the README: - 1 chefmozaccepts.csv - Instances: 1314 - Attributes: 2 - placeID: Nominal - Rpayment: Nominal, 12 - 2 chefmozcuisine.csv - Instances: 916 - Attributes: 2 - placeID: Nominal - Rcuisine: Nominal, 59 - 3 chefmozhours4.csv - Instances: 2339 - Attributes: 3 - placeID: Nominal - hours: Nominal, Range:00:00-23:30 - days: Nominal, 7 - 4 chefmozparking.csv - Instances: 702 - Attributes: 2 - placeID: Nominal - parking_lot: Nominal, 7 - 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 - smoking_area: Nominal, 5 - dress_code: Nominal, 3 - accessibility: Nominal, 3 - price: Nominal, 3 - url: Nominal, Missing: 116 - Rambience: Nominal, 2 - franchise: Nominal, 2 - area: Nominal, 2 - other_services: Nominal, 3 - 6 rating_final.csv - Instances: 1161 - Attributes: 5 - userID: Nominal - placeID: Nominal - rating: Numeric, 3 - food_rating: Numeric, 3 - service_rating: Numeric, 3 - 7 usercuisine.csv - Instances: 330 - Attributes: 2 - userID: Nominal - Rcuisine: Nominal, 103 - 8 userpayment.csv - Instances: 177 - Attributes: 2 - userID: Nominal - Upayment: Nominal, 5 - 9 userprofile - Instances: 138 - Attributes: 19 - userID: Nominal - latitude: Numeric - longitude: Numeric - the_geom_meter: Nominal (Geospatial) - smoker: Nominal - drink_level: Nominal, 3 - dress_preference:Nominal, 4 - ambience: Nominal, 3 - transport: Nominal, 3 - marital_status: Nominal, 3 - hijos: Nominal, 3 - birth_year: Nominal - interest: Nominal, 5 - personality: Nominal, 4 - religion: Nominal, 5 - activity: Nominal, 4 - color: Nominal, 8 - weight: Numeric - budget: Nominal, 3 - height: Numeric Acknowledgements This dataset was originally downloaded from the UCI ML Repository: [UCI ML][1] Creators: Rafael Ponce Medellín and Juan Gabriel González Serna rafaponce@cenidet.edu.mx, gabriel@cenidet.edu.mx Department of Computer Science. National Center for Research and Technological Development CENIDET, México Donors of database: Blanca Vargas-Govea and Juan Gabriel González Serna blanca.vargas@cenidet.edu.mx/blanca.vg@gmail.com, gabriel@cenidet.edu.mx Department of Computer Science. National Center for Research and Technological Development CENIDET, México Inspiration Use this data to create a restaurant recommender or determine which restaurants a person is most likely to visit. [1]: https://archive.ics.uci.edu/ml/datasets/Restaurant+%26+consumer+data