Recommender Systems Gaining Popularity in the Region

Big data is a collection of large volumes of data used for analysis, storage, visualization and gaining insight from this collection for various applications. One of the applications of Big Data is called a ‘Recommender System’. A Recommender System is probably the most common and simplest form of Big Data. Recommender System is a technology that provides the user with a much more personalized experience when they are engaged in online activities. 

There are different types of Recommender Systems. 
-Collaborative 
-Content based
-Demographic 
-Utility based 
-Knowledge based 
-Hybrid based

Collaborative Filtering is a technique in which preferences of users are collected and similarities are found between these users based on which recommendations are made. Demographic Recommender System is a technique in which recommendations are made on demographic classes like age groups. Content based is a technique where certain features of objects or items are identified and based on the user’s preference of these features, recommendations are made. After longer collection of such data, larger scale models are recommended. Utility based Recommender System is a technique where the system explicitly calculates user utility of an item based on which recommendations are made. Knowledge based Recommender System is a technique where the system identifies how a particular item satisfies a particular need and can calculate which needs are of higher priority to users. Hybrid based Recommender System is a combination of any of the above two techniques. 

Recommender Systems are extremely valuable to businesses that mainly run via E-Commerce and E-Marketing. Using such systems are guaranteed to procure loyal customers as most of the products and services are tuned to the preferences of the customers. The better the recommendations the higher is the tendency of the customer is to purchase items. This brings in more profit to the business. Some companies make money through advertisements that are placed throughout the website rather than selling products. These advertisements will be of the user’s interest based on searches done in other E-Commerce websites. This naturally increases the user’s browsing time. 

Such recommender systems are built using Machine Learning and Neural Networks, both subsets of Artificial Intelligence. Recommender Systems will keep improving to satisfy users and so will Artificial Intelligence.