The dataset is obtained from a recommender system prototype from UCI Machine Learning Repository of Restaurant and Consumer data Data Set. The task is to generate a top-n list of restaurants according to the consumer preferences.
The ability of these engines to recommend personalized content, based on past behavior is incredible. It brings customer delight and gives them a reason to keep returning to the website. In this post, I will cover the fundamentals of creating a recommendation system using in Python. I will get some intuition into how recommendation work and create basic popularity model and a collaborative filtering model.
Here i am using the data set of Reataurant and consumer data Data set which has been collected from UCI Machine Learning Repository (Link: https://archive.ics.uci.edu/ml/datasets/Restaurant+%26+consumer+data). The basic information about this data set is it has total nine csv file in which five of them have Restaurant category, three are from customer category and one is from rating category. You can download this file from https://archive.ics.uci.edu/ml/machine-learning databases/00232/Rcdata.zip