Chapters
1: Introduction
2: Recommendation systems
3: Item-based filtering
4: Classification
5: More on classification
6: Naïve Bayes
7: Unstructured text
8: Clustering

Contents

  • 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 PDF of the Chapter

Python code

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.

Data

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). .