A sound AI Recommendation system can help you to -
But choosing a good AI Recommendation System can be tricky.
The questions that one should ask while choosing a Recommendation System are:
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 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.
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.
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.