1
$\begingroup$

if I make an application for movies and each user in the system can rate the movies. And I want to make a recommendation system to recommend movies to active user based on his rating for other movies. using item based collaborative filtering using KNN.
when we find the similarities between the movies and pick the top k items, which approach is correct?

1- calculate the similarities between all movies and then take the top k for every movie the user rated it highly. (the dataset is a matrix represent the rating values for each item from each user)

2- The KNN is applied to all the movies that the user likes, one after the other, and we find the similarity between each movie and the films that the user did not rate, so that for each film we take the top K of similar films (the user not rated yet) for each movie the user rated highly, then show it to the user . (the dataset for each time we apply knn is a matrix contain rating for each item the user rated and all other items that the user not rated yet).

$\endgroup$

Your Answer

By clicking “Post Your Answer”, you agree to our terms of service, privacy policy and cookie policy

Browse other questions tagged or ask your own question.