Recommender engines are affected mainly by the following two issues:
The sparsity problem: Recommender engines work upon user preferences (or ratings for different items, depending upon the application) to predict or recommend products. Usually the ratings are given on some chosen scale but the user may choose not to rate certain items which he/she hasn't bought or looked at. For such cases, the rating is blank or zero. Hence, the ratings matrix R has elements of the form:
For any real world application, such as an e-commerce platform, the size of such a ratings matrix is huge due to the large number of users and items available on the platform. Even though a lot of user related information is gathered on such a platform, the ratings matrix itself might still be pretty sparse, that is the matrix might have a many elements as blanks (or zeroes). This problem in general is termed the sparsity problem. The sparsity problem renders the recommender engine's predictions...