Using item-based collaborative filtering, we can compute how similar two movies are to each other. We follow these steps:

- For every pair of movies (
**A**,**B**), we find all the users who rated both**A**and**B** - Now, using the preceding ratings, we compute a Movie
**A**vector, say**X**, and a Movie**B**vector, say**Y** - Then we calculate the correlation between
**X**and**Y** - If a user watches movie
**C**, we can then recommend the most correlated movies with it

We then compute the various vector metrics for each ratings vector **X** and **Y**, such as size, dot product, norm, and so on. We will use these metrics to compute the various similarity metrics between pairs of movies, that is, (**A**, **B**). For each movie pair (**A**, **B**), we then compute several measures ...