How to use Redis as a Vector Database for Recommendations
Introduction This blog post is the result of some preparation work for a recent meetup, where I introduced a bunch of recommendation engine algorithms. The idea of using vector similarity search for recommendations is quite simple: The interests of a user are expressed as a vector. Each component of the vector is associated with one category of interest. If we know the interests of a specific user, we can search for the K - N (earest) N (eighbours) to find other users that share the same interests. We can then inspect the behavior of these users (e.g., the purchase history) to recommend our user specific products. The following table shows the vector [0.9, 0.7, 0.2] : books comics computers 0.9 0.7 0.2 Let's assume that a user is only interested in a specific category if the interest value is larger than the threshold of 0.4 , which means that our user is interested in 'books' and 'comics' but not in 'c...
