User-based recommenders

In user-based recommenders, similar users from a given neighborhood are identified and item recommendations are given based on what similar users already bought or viewed, which a particular user did not buy or view yet.

For example, as shown in the following figure, if Nimal likes the movies Interstellar (2014) and Lucy (2014) and Sunil also likes the same movies, and in addition, if Sunil likes The Matrix (1999) as well, then we can recommend The Matrix (1999) to Nimal, as the chances are that Nimal and Sunil are like-minded people.

A real-world example – movie recommendations

Let's explain this approach using a real-world example on a movie recommendation site, as shown in the following figure:

Users who watched the movies (items) rated them according to their preferences. The rating is a value between 1 (lowest) and 10 (highest).

The user, item, and preferences (ratings) information is given in the following table; you need to save this data as movies.csv in order...