Product Recommendation System Using Machine Learning, Informatics for Technology LLC | Oman
New Technologies

Product Recommendation System Using Machine Learning

Have you ever been in an online store in order to buy something and ended up purchasing a lot of items? Or maybe you watched a video on YouTube and time flies during watching other videos for hours?

In each of those situations you feel like the service you’re using knows exactly what you’re thinking about and gives you what you want. With keeping in mind that there are a lot of personas who deal with this service with different personalities and lifestyles, you may wonder how that could be possible?

Product Recommendation System

One of the ways to do such a thing is by a Product Recommendation System (PRS). it’s a software tool that can suggest items for the customer that may satisfy his/ her needs and personality. 

This system creates a net of complex connections with the help of machine learning techniques and a lot of training data for both products and users. Those connections the system builds are the core component in the recommendation process.

The Connection Types of PRS

There’re 3 types of those connections:

1- The User-product connection: this connection is built based on the individual users with their product preferences.

2- The User-user connection: these types of connections are built between users. Those users have similar backgrounds, age, interests, etc.

3- The Product-product connection: it links between products that are similar or complementary. For example, when you add a smartphone in your cart and the store suggests you to buy a case or headphones.

Benefits of Product Recommendation System

In our everyday life, we’re stuck in PRS everywhere while searching on the web, watching videos on YouTube, buying online, and more. Each one of them has applied a PRS carefully to work fine.

And if you don’t use a PRS in your service, you’re missing these opportunities:

1- Increase Sales

It becomes a fact that a lot of companies which are using PRS have grown rapidly. With good connections in your service, you can increase the sales of poor products that no one knows by building new connections that you didn’t know before.

2- Delivering Relevant Content

If you deliver the right products towards the eyes of your customer, you will increase the customer experience while using your service.

3- Increase the Average Number of Items Per Order

For this part, it can boost your sales as well as decreasing the cost of shipping. Like for example when you deliver 4 items with the same cost of delivery of 1 item.

4- Controlling Your Merchandise

By using a PRS, you can have the ability to highlight featured products on flash sales for customers.

Disadvantages of a Product Recommendation System

1- Maybe the most challenging part in PRS is collecting data. The lack of data is one of the most issues that face a lot of businesses. As for the learning process of the PRS, ML needs a huge amount of data in order to recommend products effectively.

2- As trends change by time, we need a way to refine our connections in a good way. At some point, if we don’t update the user preferences and keep up to date with trends, we might see ourselves losing all the benefits of PRS.

References

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