How Do Recommendation Systems Work? An Important Guide
Customers drive sales, and for gaining more customers, sellers need to understand their customers, and understanding their customers is important to understand the behavioural pattern. Nowadays, e-commerce has shifted its focus on data to boost sales, and a recommendation system was introduced. It is beneficial for both users and service providers as it helps websites improve statistics and improves sales figures and customer satisfaction. In this article, we look into what is a recommendation system.
What Is A Recommendation System?
Here the recommendation system recommends a product to the users according to their interests to get engaged. Based on historical data and users past behaviour, the recommendation system creates a structure for users. The algorithms in machine learning predict items that the user might like based on past activity and give the best suggestions. This is a quite good method by which business is getting more revenues.
Recommendation systems are helpful in various platforms like music services, video recommendations, product recommendations in online stores, a recommendation in web content, and a recommendation in social media platforms.
Why Recommender Systems Matter?
Research shows that it is beneficial for both sellers and users. On the one hand, it makes the process easier for the sellers as recommendations are based on user interests. On the other hand, it reduces the cost incurred by users to find the product.
Product discovery is an important step in a user’s interaction with an e-commerce website, and recommendation systems play an instrumental part in the discovery.
This becomes ever so important with the increasing size of the catalogue, making it harder for every user to express their intent through well-formed queries.
The best part of the recommendation system is that the data is purely based on functions. Data is taken from all the applications and websites, which shows users’ likes, history, reviews, and many more related to the users’ persona.
It has been categorized into two sections
-User item interaction
Types of Recommender Systems
There are three types of recommendation systems that are available :
- Content-based filtering
- Collaborative based filtering
- Hybrid method
It is based on users initial interest and focuses on the attributes of items to create an accurate prediction. When logged in to the website, it asks to enter age and other credentials, which helps in best predictions.
Unless the data collected is false, this system works perfectly, but it can lead to the wrong prediction if users enter the wrong data. Also, when the users are new, they don’t get any recommendation as there is no data or reference.
One drawback of this system is it cannot predict items similar to the user’s interest but outside the categories specified.
Collaborative Based Filtering
Compared to content-based, this one is more successful as these are not only based on prior data. It works with data collected in the past and interacts with similar items.
You can further break it into subcategories:
It defines a model for user-item interactions to learn users and item representations from the interactions matrix.
It defines no model for user-item interactions and relies on similarities between users or items in observed interactions.
Important Triggers for Collaborative Mechanism to Work-
This system needs time to collect actionable data before giving any recommendation. For any new e-commerce website, it takes time for users to get acquainted. The situation is the same where the customer is new to any site; it takes time to understand its behaviour.
Mix content-based and collaborative filtering approaches.
Recommendation System Examples
In one of the interviews, McKinsey recommended this system and revealed the world’s biggest marketplace as one of the examples of the recommendation system. Using recommender systems, Amazon estimated sales increase was 35%.
Another renowned e-commerce website Alibaba has also revealed that they have increased its purchases by 20% by using this effective mechanism.
This article covered that a recommendation system is an important technology to combating information overload. We also know that because collaborative filtering has problems and the content-based methods address these problems, integrating both is best.
For big scale companies, it is an important computer system as it serves to increase engagement of active users and increases the frequency of customers visiting their marketplace.
No doubt, the data needs to be stored to get benefit from the recommendations. For business, this will help them generate more revenue and grow its customer to an exceptional base, but it is not recommended for all. Small scale businesses are recommended not to use this method as it will affect their financial sector as it carries immense value.
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