Several types of AI systems can be used for recommendations, the most common of which is collaborative filtering. Collaborative filtering systems work by finding similarities between users’ past behaviors – if two users have purchased similar items in the past, the system will recommend items to the second user that the first user has also bought. Other AI systems used for recommendations include content-based filtering and hybrid systems.
Content-based filtering systems make recommendations based on the similarity between the content of recommended items and a user’s interests. So, if a user is interested in a particular set of products, the content-based filtering system will recommend products to the user that are similar to those products. Hybrid systems are a combination of collaborative filtering and content-based filtering.
There are many different ways that AI systems can be used to recommend products or services to users. One way is to use the system to recommend products or services to users based on their past behavior. Another way is to use the system to recommend products or services to users based on the similarity of the content of the items being recommended.
References:
https://www.cbinsights.com/research/artificial-intelligence-trends-startups/
https://www.forbes.com/sites/forbestechcouncil/2017/11/27/how-to-leverage-ai-with-collaborative-filtering-and-recommendation-engines/#38db4dd75589