Retail

As our purchase of products and consumption of content, including news, music and video, is increasingly moving online, content providers are accumulating huge amounts of data from customer registrations and transactions. Using AI/ML systems, known as recommendation engines, they can analyze the data and offer recommendations to their customers based on their stated preferences, profiles, locations, and several other attributes.

This service is useful for customers to discover new products and services that are suited to them and also helps content providers to sell their products better. Recommendation engines are particularly useful when an buyer needs to choose an item from a potentially overwhelming number of items on offer, especially in an area where the buyer may not have the skill or knowledge to decide what is the most suitable product.

Recommender systems are used in a variety of areas, including playlist generators for video and music services, product recommenders for online stores, or content recommenders for social media platforms, and restaurants.

Data Privacy

Recommedation engines require a lot of data to work well, including user transaction history. This leads to concern about data privacy since many users may not be comfortable with the data about what they have bought or watched being stored by a third-party. Yet, without this data, a recommendation engine will not work. Thus, it is a trade-off that users have to make, whether they want data privacy or suggestions for products and services that may be useful to them.