What is recommendation system?
Today we see the recommendation systems working for us in most of the applications; these systems helping users to find and recommend items or content that a user is most likely to be interested in. Example: while visiting applications like Netflix, YouTube, Amazon, LinkedIn… we notice application recommends similar products while we read about a product or recommends based on past purchases or past browsing interests.
Again, what is recommendation systems? It’s a system which uses details of users like buying or reading patterns, their opinions, habits of their community and compare the information to reference characteristics to present the recommendations.
Why recommendation systems are needed?
Three main reasons:
- Enhanced customer experience: Recommendations speed up searches and make it easier for users to access the content they’re looking for, and surprise them with offers they would have never searched for.
- Increase revenues for product companies: As the user interests are known and understood by the application, users are more likely to be attached to the application to buy additional products or consume more content. Investing to understand what the user wants; the company gains competitive advantage and helps to retain the customer to use their application over competitors.
- Avoiding Information Overload: Recommendation system help in addressing the information overload (like Blogs, forums, wikis, news, etc.) problem by retrieving the information desired by the user based on user or similar user’s preference and interests.
The recommendation systems are built using Machine learning techniques which filter out the products that users would be interested in or would buy based on his or her previous buying history. The accurate recommendations are achieved when we have good data available about a user.
The popular recommendation techniques
- Collaborative filtering
- Content-based filtering
- Hybrid recommendation systems
….Continue reading Types of recommendation systems.