Recommendation frameworks use learning methods for making customized suggestions for data, items, or services. These recommendation systems generally have some level of interaction with the target individual. The amount of data that has been collected in recent years and the data that is being generated today proved a great boon for these recommendation systems.
Today, many recommendation systems are in operation and produce millions of recommendations per day:
Recommendations on e-commerce websites regarding the books, clothes, or items to buy
Advertisements suited to our tastes
Type of properties that we may be interested in
Travel packages suited to our tastes and budget
The current generation of recommender systems are able to make worthy recommendations and are scaled to millions of products and target users. It is required that even if the number of products or users increase, the recommender system should continue to work. But this becomes another challenge...
