- How a recommendation system works.
- How social filtering works
- How to find similar items
- Manhattan distance
- Euclidean distance
- Minkowski distance
- Pearson Correlation Coefficient
- Cosine similarity
- Implementing k-nearest neighbors in Python
- The Book Crossing dataset
The code for the initial Python example: filteringdata.py
The code for the Pearson implementation: filteringdataPearson.py
The code for the Python recommender class: recommender.py
Confused about how to run this code in Python? Check out this short getting started video.
The Book Crossing Data: BX-Dump.zip
Movie Ratings (20 movies rated on a scale of 1-5; a blank means that person didn’t see that movie). .