Candidate Generation

The purpose of candidate generation is to select the top k (let's say one-thousand) movies that you would want to consider showing as recommendations to the end-user. Therefore, the task is to select these movies from a corpus of more than a million available movies.

In this lesson, we will be looking at a few techniques to generate media candidates that will match user interests based on the user’s historical interaction with the system.

Candidate generation techniques

The candidate generation techniques are as follows:

  1. Collaborative filtering
  2. Content-based filtering
  3. Embedding-based similarity

Each method has its own strengths for selecting good candidates, and we will combine all of them together to generate a complete list before passing it on to the ranked (this will be explained in the ranking lesson).

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