Apparatus and method for recommending music content based on music age

1. A method for recommending music content by a server including a modeling module, a collection module, and a service module, the method comprising:

training, by the modeling module, a service model using training data and result data stored in a customer database for determining a spending pattern of music content based on a neural network learning;

training, by the modeling module, the service model using the training data and the result data stored in the customer database for estimating a music age based on the neural network learning;

obtaining, by the collection module, account information for an account of a user of a service which provides music content, the account information comprising a real biological age of the user of the account, the real biological age registered as personal information;

obtaining, by the collection module, based on the account information, content information and usability information, the content information comprising one or more properties of music content consumed by the account, and the usability information comprising a manner in which the music content is accessed;

estimating, by the modeling module, a music age corresponding to the account information, based on the account information, the content information, and the usability information, the music age comprising a virtual age matched with a spending pattern of music content of the user, the music age differing from the real biological age of the user of the account;

recommending, by the modeling module, additional music content based on the music age rather than the real biological age of the user of the account; and

causing, by a point of contact (POC) application, a device to play the additional music content instead of music content matching the real biological age of the user of the account;

wherein estimating, by the modeling module, the music age comprises:

outputting the spending pattern of music content corresponding to the account information by inputting the account information, the content information, and the usability information into the service model;

generating a first vector, a second vector, and a third vector from the account information, the content information, and the usability information, respectively;

generating a customer vector encoding the first vector, the second vector, and the third vector, the first vector encoding account information comprising the real biological age of the user of the account, the second vector encoding content information comprising a property of music content consumed by the account, the third vector encoding usability information comprising a type of POC used to access music content by the account;

inputting the customer vector to a neural network of the service model;

outputting, from the neural network, a plurality of probabilities comprising, for each music age candidate of a plurality of music age candidates, a probability that the account information corresponds to the respective music age candidate, the plurality of music age candidates preset according to the spending pattern of music content; and

selecting any one music age candidate from among the plurality of music age candidates having a highest probability as the music age corresponding to the account information;

wherein training the service model for estimating the music age based on the neural network learning comprises applying a first weight corresponding to the account information, a second weight corresponding to the content information, and a third weight corresponding to the usability information;

wherein outputting the spending pattern comprises outputting the spending pattern corresponding to the input account information, the content information, and the usability information;

wherein outputting the probabilities comprises outputting probabilities corresponding to the music age candidates corresponding to the spending pattern;

wherein the result data corresponds to a result of recruiting a reference group, for each real age group of a plurality of real age groups, by a certain volume and analyzing spending patterns of users for music content; and

wherein the modeling module estimates the music age of all users based on the spending pattern of the reference group.

2. The method of claim 1 , wherein at least one of the first vector, the second vector, and the third vector includes a plurality of elements corresponding to a plurality of entities, and

wherein the at least one of the first vector, the second vector, and the third vector is generated by determining values of the plurality of elements based on a history previously collected in response to the plurality of entities.

3. The method of claim 1 , wherein the estimating of the music age includes:

applying a first weight corresponding to the account information, a second weight corresponding to the content information, and a third weight corresponding to the usability information to each music age candidate of the plurality of music age candidates;

determining probabilities corresponding to each music age candidate of the plurality of music age candidates, based on the first, the second, and the third weights; and

estimating the music age corresponding to the account information, based on the probabilities.

4. The method of claim 1 , wherein the identifying of the spending pattern includes:

defining one or more time zones for the music content; and

identifying the spending pattern for each time zone of the music content based on a time when the music content is consumed, the time being included in the usability information.

5. The method of claim 1 , wherein the account information includes at least one of:

personal information stored in the account;

purchase information of a pass through the account; and

participation information of a promotion through the account.

6. The method of claim 5 , wherein the promotion includes at least one of:

a pass discount event for the music content;

an album release event corresponding to the music content;

a performance event corresponding to the music content; and

an affiliate event associated with the music content.

7. The method of claim 1 , wherein the properties of the music content include at least one of:

a title of the music content;

a genre of the music content;

an artist of the music content;

a lyric writer of the music content;

a composer of the music content;

a management company of the artist;

a type of the artist;

sound quality of the music content;

popularity of the music content; and

a type of the music content.

8. The method of claim 1 , wherein the usability information includes at least one of:

a history of using a service menu associated with the music content; and

a manner which consumes the music content.

9. The method of claim 8 , wherein the type of the POC includes at least one of a mobile dedicated app, a tablet dedicated app, a window dedicated app, a Mac dedicated app, a social network service (SNS) dedicated app, a navigation dedicated app, a vehicle dedicated app, and an artificial intelligence (AI) speaker dedicated app.

10. The method of claim 8 , wherein the manner which consumes the music content includes at least one of:

whether to repeatedly set the same music content;

consumption of music content by a recommended playlist; and

consumption of music content by a playlist directly edited by a user.

11. The method of claim 8 , wherein the manner which consumes the music content includes at least one of:

a 1-day viewing time; and

a use time zone.

12. The method of claim 1 , wherein the obtaining of the account information includes:

estimating personal information corresponding to the account, based on at least one of whether identity authentication associated with the account is performed and whether there is parental consent associated with the account.