For example, in Netflix new movies are suggested to you based on the movies you already watched or rated. Or in Amazon new products are suggested
According to the customer online shopping or watching the data of the movie, read the specific properties of the shopping products or movies from the database, according to the film or the characteristics of the product to search for a fuzzy search to find similar products, and then as your recommendation.
Track page visits and get their common attributes. Each page should contain attributes. For Amazon, it could be product type, brand, price range, review score, etc. For Netflix, it could be genre, year, starring actors/actresses, review score, etc. Do the same for purchased/ watched items. Sort them by the count.
you can take customer data attributes and generate clustering model and clustering model can give you similar movies.
A straightforward approach is creating a similarity model based on entity's attributes. Each attribute may have different weight to calculate a final score. But since comparing millions of entities is very costly, a graph database may be another good choice for recommending related entities based on common attributes
It can be built using Collaborative filtering using K nearest neighboring algorithm, by finding the K similar users and using their results to filter out suggestions.
Another approach is to use association analysis, to find association rules between two or more than two movies and based on that suggesting, it uses Apriori Algorithm
I think this is a big topic about information search, you may refer to opensource lucene (https://lucene.apache.org) especially about boost sorting chapter.
Looks like your connection to LeetCode Discuss was lost, please wait while we try to reconnect.