Recommender system is a data filtering tool used to predict users interests or preferences, according to their historical data stored on a database. It is a way to understand your clients’ needs based on their past activities and make suggestions about what they will probably like, improving customer experience and satisfaction.
Also called as a recommendation engine, this feature developed with AI (artificial intelligence) allows companies to be much more assertive offering its products or services to customers according to their historical behavior (clicks, purchases, likes, rates, etc.). Recommender systems have a very accurate process, once it utilizes machine learn algorithms.
This computer program is applied in many different channels, such as commercial apps, e-commerce, social media, content-based services and others. It is used to facilitate users’ choices, once they will get suggestion based on their preferences and tastes previously shown.
There are some different approaches which can drive the recommendation engines:
Collaborative filtering – This is based on previous information about customers activities. The engine analyzes data and makes the assumption that if users had some types of agreements in the past, they will keep having the same in the future.
It tracks their behaviors and preferences to identify patterns and provide recommendations based on similarity of users. One good example is the recommendation made by Netflix or Spotify – which offers users types of films or music according to their previous preferences and other users preferences similarly – they consider that if User1 likes items A, B and C, while User2 likes items A and B, there are big chances that User2 will also like item C.
Content-based filtering – In this case, the system uses its knowledge of each product to recommend new products. It knows the attributes that all the products have and match them with customers’ previous preferences. As an example, we can think about Amazon – You buy a drama genre book from author X, then the system will recommend you another drama genre book, which was written by the same author because the engine understands that they have a lot of attributes in common.
Hybrid recommendation systems – This approach uses information from both collaborative and content-based filtering, which are user-item interactions and items’ characteristics. Its performance can be even better than the other two single approaches, because it will cover any gap that exists in each of them separated. This system is much more complete and ensure a better result for users.
Understanding recommender system, it is possible to see how good and powerful this feature can be for businesses and also for users. It provides much time saving and precision for customers when they are looking for some product or entertainment and helps companies to be much more effective on their offers.