Recommendation systems as the apotheosis of the consumer society
The social recommendation systems are based on specific algorithms that analyze user preferences, recognize common patterns, and try to predict which product might still be of interest to a particular user.
Modern systems of recommendations are able to create the most accurate psychological portrait of a person based on minimal basic data: it suffices to know several of his favorite films and books. Sometimes a single name is enough (if it is not very common). After that, you can confidently predict what products this person wants to buy, and indeed in general - to which class of consumers he belongs in terms of psychography .
In a developed consumer society, any recommendation system capable of raising sales by at least 1% will bring a whole fortune to its creator. For the creation of such a system right now you can get a prize of one million dollars . Not surprisingly, recently, in the wake of the Web 2.0 boom on the American Internet, one after another , new recommendation engines have emerged in various fields: for music, books, websites, television shows, other people, etc.
Previously, the system of recommendations worked only in certain groups of goods. For example, knowing about buying some books, they could recommend other books. Now, such systems are able to “transform” preferences from one commodity reality into another. Knowing your favorite movie - pick clothes. According to the information about the brand of car, choose home furniture. The system is able to pick up goods for the psychological profile of a person.
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For example, in the What to Rent system you must pass an unusual psychological test of twenty questions, including tell about your first love. After that, you need to indicate in what mood you are right now. The system analyzes all the information and displays the name of the movie that suits you best. If you have already watched it, then another one appears.
The system was created by two technical students and a video man at the same time. At first glance they can identify the favorite movie of almost every person on the street, and now they decided to create a computer system that does the same.
Recommendation systems like What to Rent appear on the Internet like mushrooms after a rain: MyStrands , StumbleUpon , Pandora.com , CleverSet , ChoiceStream (this engine is used in iTunes and DirecTV) and many others.
Of course, the idea is not new. Large retail chains, representatives of the advertising industry have long solved this problem. Despite some success (the emergence of smart online advertising ), research in this direction continues.
In a world where consumption and advertising prevail, every person has a consumer pattern. Each of us gives preference to one or another brand, brand, certain goods. Even if a person specifically ignores brands - this is also a definite pattern that clearly indicates what and how you can sell to such a person. Modern systems of recommendations aim to find such patterns.
This is a good, very promising business. Especially now, when every person has an archive of digital data, according to which it is possible to accurately build his consumer portrait in a split second. Archive digital music in the MP3-player, a collection of digital films, the history of surfing, tags and bookmarks on Web 2.0 sites - and ready. After analyzing this information, the person was hooked by the marketer. Now he will receive commercial offers that he cannot refuse. Things are easy - to create a universal system that can automate this process.
Over this complex task hundreds of different companies are working. Even the former Kiev resident Max Levchin (founder of Paypal ) has created a new startup Slide in this particular area. He says, by the way, that Paypal is also equipped with something like a recommendation system: they analyze the profile of each user and calculate the likelihood of fraud on his part.
Perhaps the next giant of Internet search, the so-called “smart Google” , will be created precisely on the basis of these new wave companies.
Modern systems of recommendations are able to create the most accurate psychological portrait of a person based on minimal basic data: it suffices to know several of his favorite films and books. Sometimes a single name is enough (if it is not very common). After that, you can confidently predict what products this person wants to buy, and indeed in general - to which class of consumers he belongs in terms of psychography .
In a developed consumer society, any recommendation system capable of raising sales by at least 1% will bring a whole fortune to its creator. For the creation of such a system right now you can get a prize of one million dollars . Not surprisingly, recently, in the wake of the Web 2.0 boom on the American Internet, one after another , new recommendation engines have emerged in various fields: for music, books, websites, television shows, other people, etc.
Previously, the system of recommendations worked only in certain groups of goods. For example, knowing about buying some books, they could recommend other books. Now, such systems are able to “transform” preferences from one commodity reality into another. Knowing your favorite movie - pick clothes. According to the information about the brand of car, choose home furniture. The system is able to pick up goods for the psychological profile of a person.
')
For example, in the What to Rent system you must pass an unusual psychological test of twenty questions, including tell about your first love. After that, you need to indicate in what mood you are right now. The system analyzes all the information and displays the name of the movie that suits you best. If you have already watched it, then another one appears.
The system was created by two technical students and a video man at the same time. At first glance they can identify the favorite movie of almost every person on the street, and now they decided to create a computer system that does the same.
Recommendation systems like What to Rent appear on the Internet like mushrooms after a rain: MyStrands , StumbleUpon , Pandora.com , CleverSet , ChoiceStream (this engine is used in iTunes and DirecTV) and many others.
Of course, the idea is not new. Large retail chains, representatives of the advertising industry have long solved this problem. Despite some success (the emergence of smart online advertising ), research in this direction continues.
In a world where consumption and advertising prevail, every person has a consumer pattern. Each of us gives preference to one or another brand, brand, certain goods. Even if a person specifically ignores brands - this is also a definite pattern that clearly indicates what and how you can sell to such a person. Modern systems of recommendations aim to find such patterns.
This is a good, very promising business. Especially now, when every person has an archive of digital data, according to which it is possible to accurately build his consumer portrait in a split second. Archive digital music in the MP3-player, a collection of digital films, the history of surfing, tags and bookmarks on Web 2.0 sites - and ready. After analyzing this information, the person was hooked by the marketer. Now he will receive commercial offers that he cannot refuse. Things are easy - to create a universal system that can automate this process.
Over this complex task hundreds of different companies are working. Even the former Kiev resident Max Levchin (founder of Paypal ) has created a new startup Slide in this particular area. He says, by the way, that Paypal is also equipped with something like a recommendation system: they analyze the profile of each user and calculate the likelihood of fraud on his part.
Perhaps the next giant of Internet search, the so-called “smart Google” , will be created precisely on the basis of these new wave companies.
Source: https://habr.com/ru/post/4701/
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