‍For example, Netflix uses the three-legged tool as its recommendation tool. At least 80% of their viewers discover new shows with the help of their recommendation system.

Benefits of AI-based Recommendation System

A sound AI Recommendation system can help you to -

• Personalise the experience of your customers
• Help you to target customers to promote your products
• Record data and customer reactions for future use
• Showcasing popular, more attractive items for engaging customers

• Attain higher profits
• Higher conversion rates
• Higher average order value
• Increase in customer base and loyalty.
  

But choosing a good AI Recommendation System can be tricky.

The questions that one should ask while choosing a Recommendation System are:

1. Do these recommendations help the customers?
2. Do these suggestions modify customer behaviour into preferring your products?
3. Are these suggestions narrowed down to the customer’s likes, or are they too broad?

Foundation for Recommendation

The essential part of employing an AI Recommendation System is the availability of enough information. Recommendation systems can work efficiently only if there is enough information provided to them. Without context, they cannot perform accurately.

After getting enough accurate data, your data science and engineering team would have to modify the model while considering which filtering criteria you wish to opt for.

Content-Based Filtering

Content-based filtering generally refers to the information and recommendations made based on the overall content’s information.

For example, if someone is buying a particular shoe, the content would be the colour, material, designer, price, etc.

The recommendation would be made by generating preferences based on the profiles of items in the customers’ history and the connections and best possible matches between those products and the products offered by your company.

This approach is best suited for companies who are still in their early stages of operations. This is because this system does not depend on historical data or highly active customer bases.

Collaborative-based Filtering

If your company already has a working customer base, this system is more suited for your needs. This filtering system uses similarities between two users to give recommendations. This system focuses on rating or purchase history to compare users.

Here, they make connections between two users and your company’s products and then suggest products. This system of choosing is more useful for people who have very particular tastes in products.

The music streaming app Pandora uses this collaborative-based filtering approach.

Conclusion

To conclude, we can say that Recommendation Systems are beneficial for enhancing your business. So, one must carefully choose a system after thorough consideration, making sure it meets their business needs.