• Now that we know all the feature in each of the product, we may want to know relatedness between them
  • It's often difficult as it's more like human understanding about each product, but it certainly plaussible
  • What might be the case in movie examples, is suppose the users purchase some products, and we may want to search of other similar products to recommend to them
  • This is reasonable as we want users to keep engaging and purchasing our product
  • So what we do is, we are trying to find the smallest distance about other products that user purchased, the smaller the distance is, then the two products is more related
  • With this, we can find 5 of the smallest distance, and recommend 5 of most similar movies that we can recommend to the users

  • By now, we already know about the vectorized implementation of Collaboration Filtering algoritm
  • And we also learn how to use features of the movies, and find similar movies to recommend to users.