User-based collaborative filtering

In the previous section, the algorithm was based on items and the steps to identify recommendations were as follows:

  • Identify which items are similar in terms of having been purchased by the same people
  • Recommend to a new user the items that are similar to its purchases

In this section, we will use the opposite approach. First, given a new user, we will identify its similar users. Then, we will recommend the top-rated items purchased by similar users. This approach is called user-based collaborative filtering. For each new user, these are the steps:

  1. Measure how similar each user is to the new one. Like IBCF, popular similarity measures are correlation and cosine.
  2. Identify the most similar users. The options are:

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