This project is a recommender system that recommends a new beer a user might like.
- https://www.kaggle.com/rdoume/beerreviews
- 1.5 million reviews
- 66,055 unique beers
- 33,388 unique users
- ratings on 4 different beer attribute: taste, appearance, palate and aroma
- 1 overall rating
- When two or more users give a similar 'overall' rating for same kind of beers but different ratings for individual beer attribute
- For example, Beer 1 might have received an overall rating of 4 from User 1 and User 2 but it might have received totally different ratings of the beer's attributes
- In such cases, a recommender system based only on overall rating cannot figure out the user's actual preference
- To create a recommender system that aggregates the result of 4 single-rating recommender system based on each of the beer attributes and their weighted average.
- The weight average can either come explicity from the user or by observing the correlation of each rating with the 'overall' rating.
- scikit-surprise
- A 3 step application to get your recommended beer
- Made using ReactJS
This project was part of a project for a course called Data Mining at KTH Royal Institute of Technology. It was done together with Heeje Lee (https://github.com/zedshape), Balint Kovacs and Yu-wen Huang.