Recommendations are a collaborative filtering problem in the machine learning space. Two underlying principles define how collaborative filtering algorithms work:
- Filtering
- Collaborative
The filtering part is associated with the act of recommending. The algorithm makes recommendations happen by ingesting preferences information from many users. A simple example will go a long way in illustrating how collaborative filtering algorithms work. Imagine that our algorithm is working off of a pool of three users (countries) U1, U2, and U3. However trivial this case may be, it will explain how collaborative filtering algorithms work. Say, at a recent global air show, countries looking for new fighter aircraft ...
