Technical Content Writer at almaBetter
Of late, there has been a surge in the use of recommendation systems by e-commerce and retail companies in order to boost sales.
With the growing popularity of online shopping, the need for recommendation systems that can provide confidence in buying products has also increased.
In this blog post, we will look at the different types of popular recommendation systems, how they work, and how they can be used.
A recommendation system is a computer program that recommends items for users of digital platforms such as e-commerce websites and social networks. It uses large data sets to develop models of users’ likes and interests, and then recommends similar or recommended items to individual users.
For example, Amazon’s recommendation system suggests items to customers based on their previous purchases and browsing behaviour. Facebook uses a recommendation system to suggest new friends to users, and Netflix uses a recommendation system to recommend movies and TV shows to users.
A recommendation system primarily processes data through four stages, which are as follows:
Data collected can be both explicit and implicit. For example, ratings and comments on products would be explicit data, while page views and order history would be implicit data.
The type of data used to create recommendations can help you decide the kind of storage you should use.
NoSQL databases are good for handling large volumes of data, object storage is good for storing large amounts of data, and standard SQL databases are good for handling smaller amounts of data.
The recommender system finds items with similar user engagement data after analysis and recommends them to the user.
The last step is to choose an algorithm that will help filter the data to provide the user with relevant recommendations.
Machine learning can be used to solve many problems, but one of its most well-known applications is making product recommendations. There are three main types of recommendation systems –
The collaborative filtering approach gathers data on user behavior and analyzes it to predict what individual users will like. This is done by finding similarities between users and using those to make predictions.
For example, User A likes movies of the genre action, adventure, and science fiction. User B likes movies of the genre action, adventure, and fantasy. They have similar interests. So, it is highly likely that A would like movies of the genre fantasy and B would enjoy movies of the genre science fiction. This is how collaborative filtering takes place.
The two kinds of collaborative filtering techniques used are:
This recommendation system’s ability to make precise recommendations without having any prior knowledge of the recommended item is one of its main benefits. There is no reliance on content that can be analyzed by computers.
Content-based filtering methods rely on a product description and a user profile to determine the user’s preferred choices. Products are described using keywords in this recommendation system, and a user profile is created to express the type of item this user prefers.
For instance, if a user likes to watch movies such as The Shawshank Redemption, the recommender system recommends movies of the drama/crime genre or films based on Stephen King novels.
The central assumption of content-based filtering is that you will also like an item if you like similar items.
To suggest a broader range of products to customers, hybrid recommendation systems use both content-based and collaborative filtering at the same time. This is a new recommendation system that is said to provide more accurate recommendations than other recommendation systems.
amazon.com is an excellent example of a hybrid recommendation system. It makes recommendations by juxtaposing users’ buying and searching habits and finding similar users on that platform. This is how amazon.com uses collaborative filtering.
By recommending such products that share similar traits with those rated highly by the user, amazon.comuses content-based filtering. They can also veto the common issues in recommendation systems, such as cold start and data insufficiency issues.
There are many different types of recommendation systems, each with its own advantages and disadvantages. The most important thing is to choose the right type of system for your needs.
If you need a system that can handle a large amount of data, then a collaborative filtering system may be the best choice.
If you need a system that can recommend items to users based on their past behavior, then a content-based system may be the best choice.
Ultimately, the best recommendation system is the one that meets your needs and provides the best results for your users.
Read our latest blog on “3 best ways to handle right-skewed data”.