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

Implicit Ratings and Item Based Filtering

This chapter starts with a discussion of the types of user ratings we can use. Users can explicitly give ratings (thumbs up, thumbs down, 5 stars, or whatever) or they can rate products implicitly–if they buy an mp3 from Amazon, we can view that purchase as a ‘like’ rating.


  • Explicit ratings
  • Implicit ratings
  • Which is more accurate: explicit or implicit?
  • User-based filtering
  • Item-based filtering
  • Adjusted Cosine Similarity
  • Slope One Algorithm
  • Python code for Slope One
  • MovieLens data

The PDF of the Chapter

Python code

There is only one Python file for this chapter:


In addition to the data set introduced in chapter 2, this chapter uses the MovieLens dataset available from The dataset used in this chapter is the smallest one on that site–the 100,000 rating one.