Use Euclidean similarity measure to find nearest neighbors for music recommendation.
- Create a music recommendation system
- Given a dataset of users, write code to find the Nearest Neighbor recommendations for user X based on Euclidean similarity measure
- Find artists that other users in the data set have rated to recommend artists that user X may also like
- Personlaize recommendations Utility Matrix - Model
- Utility Function u: X * I -> R
- X = set of Customers
- I = Set of Items
- R = set of ratings
- R is a totally ordered set
- e.g., 0-5 Stars, real number [0,1]
- Gathering "known" ratings for the matrix
- How to collect the data in the utility matrix
- Extrapolate unknown ratings from the known ones
- Mainly interested in high unknown ratings
- We are not interested in knowing what you don't like but what you do like
- Evaluating extrapolation methods
- How to measure success/performance of recommendation methods