Collaborative Filtering: Non-Personalized item-to-item similarity

Question

I\'m trying to compute item-to-item similarity along the lines of Amazon\'s \"Customers who viewed/purchased X have also viewed/purchased Y and Z\". All of the examples and

Answer 1

This may not be a perfect answer for your question but another way to look at this problem is Frequent Itemset Mining, which computes all the frequently co-purchased product pairs / groups given a minimum frequency threshold. And you can map a customer's purchase to its commonly co-purchased products.

There is no model training or Bayesian probability predicting because it's a pure math problem. Just need to count the frequency of all possible product pairs purchased together in your transaction base. It's an exponential search space but there are a lot of different efficient algorithms and implementations out there to use (SPMF is a very good one written in Java). This could work as a quick baseline model.