This dataset was obtained from a recommender system prototype, and the task is to generate a top-n list of restaurants according to the consumer preferences. Two approaches can be used: a collaborative filtering technique and a content-based approach.
The data were originally from the UCI Machine Learning Repository. There are a README and nine csv files in the data directory, including five for the restaurant information, three for the consumer information, and one for the ratings:
Restaurants
- chefmozaccepts.csv
- chefmozcuisine.csv
- chefmozhours4.csv
- chefmozparking.csv
- geoplaces2.csv
Consumers
- usercuisine.csv
- userpayment.csv
- userprofile.csv
Ratings
- rating_final.csv
Three ratings (rating, food rating, and service rating) with values of 0, 1, or 2 are given for a restaurant-consumer pair. More detailed descriptions of the data can be found in the README.
Project-Hsu.pdf is a written report for this project. For the code, several collaborative filtering approachs are shown in collaborative_filtering.ipynb and gibbs_sampling.ipynb. Data exploration and visualization (in preparation for a content-based approach) are shown in exploration.ipynb. And content_based.ipynb shows the content-based approach.
Dataset 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