The essence of a recommender system
Posted: Mon Jan 20, 2025 10:35 am
Recommender systems were first discussed in the 1990s. Already in the 2000s, they became an important element of e-commerce, and leading global brands quickly felt the economic benefits of using this tool. Its most important advantages:
Increase sales.
Maintaining audience loyalty and reducing churn.
Increase in profits and average oman mobile phone numbers database purchase price.
The essence of a recommender system
The experience of the first use of smart recommendations showed that this tool has broad prospects not only in sales, but also in many other web services. The first to actively use the system was the streaming platform Netflix. The effect was as follows:
Clients, receiving personalized recommendations, continued to use the service and did not look for content on other resources.
Sales and views have increased.
Netflix's experience has attracted the attention of other major services. Today, smart recommendations are used on almost all reputable Internet platforms, be it an online store or an online cinema. Recommender systems not only help retain users and increase the average check, but also allow you to directly shape customer preferences. Many of us are familiar with this situation. Let's look at a specific example.
Let's say you decide to take up bodybuilding, but you have neither knowledge of training systems nor skills in handling sports equipment. If you start searching social networks for communities dedicated to this topic, then over the next few days the user will receive recommendations from other groups and users talking about body building, from which the data will be drawn. Thus, this system forms a limited information space for each consumer.
Forming smart recommendations is a complex task in terms of data analysis and technical implementation. It is solved based on a number of simpler mathematical algorithms. To better understand how recommendation systems are created, let's look at a typical marketplace. If a person is viewing information about a product, then several more products will be offered to him in the adjacent menu. At least some of them are of interest to the user and quite correspond to his preferences.
Increase sales.
Maintaining audience loyalty and reducing churn.
Increase in profits and average oman mobile phone numbers database purchase price.
The essence of a recommender system
The experience of the first use of smart recommendations showed that this tool has broad prospects not only in sales, but also in many other web services. The first to actively use the system was the streaming platform Netflix. The effect was as follows:
Clients, receiving personalized recommendations, continued to use the service and did not look for content on other resources.
Sales and views have increased.
Netflix's experience has attracted the attention of other major services. Today, smart recommendations are used on almost all reputable Internet platforms, be it an online store or an online cinema. Recommender systems not only help retain users and increase the average check, but also allow you to directly shape customer preferences. Many of us are familiar with this situation. Let's look at a specific example.
Let's say you decide to take up bodybuilding, but you have neither knowledge of training systems nor skills in handling sports equipment. If you start searching social networks for communities dedicated to this topic, then over the next few days the user will receive recommendations from other groups and users talking about body building, from which the data will be drawn. Thus, this system forms a limited information space for each consumer.
Forming smart recommendations is a complex task in terms of data analysis and technical implementation. It is solved based on a number of simpler mathematical algorithms. To better understand how recommendation systems are created, let's look at a typical marketplace. If a person is viewing information about a product, then several more products will be offered to him in the adjacent menu. At least some of them are of interest to the user and quite correspond to his preferences.