Matrix Factorization in Recommendation Systems | aiensured.com

Matrix Factorization in Recommendation Systems
Recommendation systems are very often encountered in our daily browsing whether while shopping on e-commerce platforms or watching OTT platforms. In these contexts, the recommendations systems make use of multiple sources of information including details of the items watched or purchased, their characteristics and extent to which you have watched an item or repeat buying of an item, etc. One additional piece of information which is also often used to determine the right recommendation is the use of the rating information provided by users.
The rating information includes users who provide ratings to items and this rating information is used by techniques like matrix factorization to extract latent information like the kind of items a user likes, to the different grades to which a characteristic occurs in an item. So using the matrix factorization technique we can extract hidden information of users' hidden interests to guide on right recommendations which will be quite relevant based on the other users' ratings.
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