Hands-On Recommendation Systems with Python

Item-based collaborative filtering

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. ...