Java Data Analysis

Amazon's item-to-item collaborative filtering recommender

The recommender algorithm that Amazon adopted early on was an improvement of the Recommender1 implemented previously. The main difference is in step 5, where now we have two more substeps, which pick the n₁ most similar items and sort them by popularity:

Recommender algorithm 2 is as follows:

1. Initialize the utility matrix (u_ij) with m rows and n columns, where m is the number of users and n is the number of items.
2. For each pair (i, j) in the input list, set u_ij = 1.
3. Initialize the similarity matrix (s_jk) with n rows and n columns.
4. For each j = 1…n and each k = 1…n, set s_jk = s(u, v), the cosine similarity of the j^th column u and the k^th column v of the utility matrix.
5. For a given user-purchase ...