Content-based methods try to use the content or attributes of the item, together with some notion of similarity between two pieces of content, to generate similar items with respect to the given item. In this case, cosine similarity is used to determine the nearest user or item to provide recommendations.
Example: If you buy a book, then there is a high chance you'll buy related books which have frequently gone together with all the other customers, and so on.
As we will be working on this concept, it would be nice to reiterate the basics. Cosine similarity is a measure of similarity between two nonzero vectors of an inner product space that measures the cosine of the angle between them. Cosine of 00 is 1 and it is less than 1 for any other angle:
Here, Ai and Bi are components of vector A and B respectively:
Example: Let us assume A = [2, 1, 0, 2, 0, 1, 1, 1], B = [2, 1, 1, 1, 1, 0, 1, 1] are the two vectors and we would like to calculate the cosine...