A recommender system aims to suggest products, services or items based on their prediction of user’s interests. It automatically recommends content on the internet to website users and readers. The system recommends to the users only those products that the users might find them interesting. It is one of the most powerful tools, which are used by online sellers for increasing sales.
In the last few years, because of Amazon, Youtube and many other giant web service providers, the recommender systems have gained a lot of popularity. In e-commerce, these systems suggest those products that would interest the buyers. And, in an online advertisement, these systems would suggest the right content to the users. The recommender systems are very important in some fields because they can drive a lot of traffic and generate huge income if they are efficiently used. These systems can also help the companies to stand out from their competitors.
The recommender systems get the required data from ratings given by the users after purchasing a product or watching a movie and even from search engine queries. Online sites such as Amazon use that data for recommending products to the users. And, music platforms such as YouTube and Netflix use the collected data for suggesting playlists and make video recommendations.
Reasons to use Recommender Systems
- The focus of companies that use recommender systems is on increasing sales. This is done by improving customer experience and giving personalized offers.
- Recommendations given by recommender systems speed up searches and allow users to access that content in which they are interested. The users would also get surprise offers on those products and services.
- The recommender systems help companies to stay ahead of their rivals and increase their earnings.
- Companies using these systems can send emails that would contain links to offers based on the receiver’s interests.
- Once the users start receiving recommendations based on their interests, they began to trust the company. They are more likely to stay and consume more content or buy more products from that company.
Recommender system works with two types of information. One is characteristic information and another is user-item interactions. Then, there are three algorithms that are used in these systems-Content based systems that use characteristic information, collaborative filtering systems and hybrid systems.
The main aim of the recommender system is to recommend relevant items and services to the users. This can be achieved by these methods-content based and collaborative filtering systems. Both of these methods are used according to various aspects and needs. But when the recommender system is implemented successfully, the companies are sure to get results.