Recommender systems are a type of machine learning system that learns user preferences to predict what users may like. They are used in a variety of applications, including:

• E-commerce: Recommending products to users based on their past purchases and browsing history.

• Streaming services: Recommending movies, TV shows, music, and podcasts to users based on their watch history and listening habits.

• Social media: Recommending friends, groups, and content to users based on their interests and connections.

• News services: Recommending articles to users based on their reading history and topics they follow.

Semantic recommender systems utilise vector representations to capture features of items and use vector search to find similar ones. Benefits include: 

• More accurate recommendations: Vector databases can be used to store and search vector representations of users and items, which can capture more complex and nuanced relationships than traditional relational databases.

• Multi-modal recommendations: Support more complex and innovative recommendation scenarios. For example, a recommender system could use vector search to recommend products to users based on their visual preferences, or to recommend songs to users based on their mood.