What are the various strategies used by recommendations engines?

There are two strategies used by recommendations engine

a) Content based filtering

This filtering is based on the description or some data provided for that product. The system finds the similarity between products based on its context or description. The user’s previous history is taken into account to find similar products the user may like.

For example, if a user likes movies such as ‘Mission Impossible’ then we can recommend him the movies of ‘Tom Cruise’ or movies with the genre ‘Action’

b) Collaborative filtering:

The recommendations are done based on the user’s behavior. History of the user plays an important role. For example, if the user ‘A’ likes ‘Coldplay’, ‘The Linkin Park’ and ‘Britney Spears’ while the user ‘B’ likes ‘Coldplay’, ‘The Linkin Park’ and ‘Taylor Swift’ then they have similar interests. So, there is a huge probability that the user ‘A’ would like ‘Taylor Swift’ and the user ‘B’ would like ‘Britney Spears’. This is the way collaborative filtering is done.

Here again there are two strategies

-User-User collaborative filtering

-Item-Item collaborative filtering

User-User collaborative filtering

In this, the user vector includes all the items purchased by the user and rating given for each particular product. The similarity is calculated between users using an n*n matrix in which n is the number of users present. The similarity is calculated using the same cosine similarity formula. Now, the recommending matrix is calculated. In this, the rating is multiplied by the similarity between the users who have bought this item and the user to which item has to be recommended. This value is calculated for all items that are new for that user and are sorted in descending order. Then the top items are recommended to that user.