Introduction
With recommender systems, machine learning systems have largely helped businesses identify new products and services offered. When viewers go to YouTube a video suggestion is presented to the viewer based on the information and historical data of the visitor. This is the core function of the recommender system. They are crucial parts of our digital world; they have features that help users to identify what they are looking for in the noisy digital space. For businesses, this means happy customers and more sales.
Generally, a recommender system gives users better experiences at the same time exposing them to more hidden and relevant inventory. It has become a household tool and big corporations are adopting its use. Companies like YouTube, Amazon and Facebook are among the big users of recommender systems.
How Recommender System Works?
The recommender system uses relations to understand customers and relay relevant insight. There are three types of relations:
| Type of Relation | Description |
| User–Product Relations | This happens when some users show a preference for certain products. For example, a football player might have a preference towards items related to football. Thus an eCommerce platform will build a user-product relation to recommend items related to football. |
| Product – Products Relations | This occurs when products have certain similarities. They could be books of the same genre, clothes of the same fashion or from the same designer et cetera. |
| User – User relations | This occurs when a customers have similar or mutual tastes and preferences to a service or a product. This includes mutual friends, colleagues, family and other similarities. |
Data Used in Recommender Systems
Recommender systems use specific types of data, namely:
| Type of Data | Description |
| User Behavior Data | User behaviour shows the nature of the connection of the user to the product. It mines the data from reviews, purchase history and clicks. |
| User Demographic Data | User demographic data shows detailed ad personal information about the user. It can be education, age, location and sex et cetera. |
| Product Attribute Data | This is the information specifically about the product. This could be genre, cast, cuisine et cetera. |
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