Item-based collaborative filtering is essentially user-based collaborative filtering where the users now play the role that items played, and vice versa.
In item-based collaborative filtering, we compute the pairwise similarity of every item in the inventory. Then, given user_id and movie_id, we compute the weighted mean of the ratings given by the user to all the items they have rated. The basic idea behind this model is that a particular user is likely to rate two items that are similar to each other similarly.
Building an item-based collaborative filter is left as an exercise to the reader. The steps involved are exactly the same except now, as mentioned earlier, the movies and users have swapped places.