Subscription based travel service with delay

1. A computer-implemented method comprising:

receiving, by one or more processors, travel information with a travel date for a user;

computing, by the one or more processors, a subscription value as a function of a booking date and the travel date, the subscription value comprising an accumulated value portion and an amortized value portion, the accumulated value portion calculated by determining a time interval between the booking date and the travel date and accumulating the subscription value over the time interval;

determining, by the one or more processors, a minimum travel value and a maximum purchase amount based on the subscription value;

training a machine-learning model to associate classifications with users based on travel services activities of the users and using a log odds model to estimate coefficients of the machine-learning model, wherein the travel services activities of the users used to train the machine-learning model comprise quantities of reservations made by the users, subscription durations of the users, distances to travel destinations for the users, margin amounts for the users, reservation frequencies of the users, and cancelation frequencies of cancelations by the users, and wherein the classifications represent activity levels for the users;

generating a classification for the user based on (i) the coefficients of the machine-learning model, as trained, and (ii) travel activities of the user;

selecting a subset of candidate travel services by filtering candidate travel services based on (i) the classification of the user generated by the machine-learning model, as trained, (ii) the travel date, (iii) the minimum travel value, and (iv) the maximum purchase amount;

receiving a request from the user via an interactive graphical user interface to reserve a given travel service having a first cost that exceeds the minimum travel value and a second cost that is less than the maximum purchase amount;

identifying a travel end date for the given travel service; and

preventing the user from reserving an additional travel service for a specified period of time of the travel end date, the preventing including disallowing the user to search for the additional travel service within the interactive graphical user interface.

2. The computer-implemented method of claim 1 , wherein selecting the subset of candidate travel services further comprises:

searching, by the one or more processors, a list of travel services that are available on the travel date to identify candidate travel services each having an additional cost that exceeds the minimum travel value;

selecting, by the one or more processors, the subset of the candidate travel services that each have a further cost that is less than the maximum purchase amount; and

generating, by the one or more processors, for display in the interactive graphical user interface to the user, one or more interactive visual representations of the subset of the candidate travel services.

3. The computer-implemented method of claim 1 , wherein the specified period of time is at least one day.

4. The computer-implemented method of claim 1 , further comprising:

determining a number of months between the booking date and the travel date, wherein the subscription value is computed as a function of a monthly subscription cost to the user and the number of months.

5. The computer-implemented method of claim 1 , further comprising computing the amortized value portion of the subscription value by:

determining an annual cost of a subscription of the user;

dividing the annual cost by a specified repeated time interval in a year;

determining a number of times the time interval repeats between the booking date and the travel date; and

computing the amortized value portion as a function of the annual cost, as divided, and the number of times.

6. The computer-implemented method of claim 1 , wherein the minimum travel value is determined based on a percentage of the accumulated value portion.

7. The computer-implemented method of claim 1 , wherein the maximum purchase amount is determined as a function of the amortized value portion and the accumulated value portion.

8. The computer-implemented method of claim 7 , wherein the maximum purchase amount is determined based on an adjusted average of the amortized value portion and the accumulated value portion.

9. The computer-implemented method of claim 8 , wherein the adjusted average is adjusted based on an expected margin value, wherein the expected margin value is positive or negative, and wherein the expected margin value is computed based on at least one of a length of time between the booking date and the travel date or a type of travel service.

10. The computer-implemented method of claim 1 , wherein the specified period of time is based on a user profile or is computed based on an attribute of the given travel service.

11. The computer-implemented method of claim 1 , wherein the specified period of time for a first user is different from the specified period of time for a second user.

12. The computer-implemented method of claim 1 , wherein the user is a first user, the specified period of time is a first period of time, further comprising:

computing a minimum travel value for a second user and a maximum purchase amount for the second user;

receiving a request from the second user to reserve a second travel service having a third cost that exceeds the minimum travel value for the second user and a fourth cost that is less than the maximum purchase amount of the second user; and

preventing the second user from reserving a third travel service for a second period of time of a travel end date of the second travel service.

13. The computer-implemented method of claim 12 , further comprising enabling the first user to reserve another travel service after the first period of time elapses and enabling the second user to reserve the third travel service after the second period of time elapses.

14. The computer-implemented method of claim 1 , further comprising:

receiving a user selection of a visual representation via the interactive graphical user interface; and

reserving the given travel service for the user associated with the visual representation, as selected.

15. The computer-implemented method of claim 1 , further comprising:

enabling the user to interact with the interactive graphical user interface to sort travel services based on a predicted likelihood that the user will reserve the travel services using a preference technique or a recommendation technique.

16. The computer-implemented method of claim 1 , wherein a first user classification indicates user activity with a subscription service that exceeds a threshold amount and a second user classification indicates user activity with the subscription service that is less than the threshold amount.

17. A system comprising:

a memory that stores instructions; and

one or more processors on a server configured by the instructions to perform operations comprising:

receiving travel information with a travel date for a user;

computing a subscription value as a function of a booking date and the travel date, the subscription value comprising an accumulated value portion and an amortized value portion, the accumulated value portion calculated by determining a time interval between the booking date and the travel date and accumulating the subscription value over the time interval;

determining a minimum travel value and a maximum purchase amount based on the subscription value;

training a machine-learning model to associate classifications with users based on travel services activities of the users and using a log odds model to estimate coefficients of the machine-learning model, wherein the travel services activities of the users used to train the machine-learning model comprise quantities of reservations made by the users, subscription durations of the users, distances to travel destinations for the users, margin amounts for the users, reservation frequencies of the users, and cancelation frequencies of cancelations by the users, and wherein the classifications represent activity levels for the users;

generating a classification for the user based on (i) the coefficients of the machine-learning model, as trained, and (ii) travel activities of the user;

selecting a subset of candidate travel services by filtering candidate travel services based on (i) the classification of the user generated by the machine-learning model, as trained, (ii) the travel date, (iii) the minimum travel value, and (iv) the maximum purchase amount;

receiving a request from the user via an interactive graphical user interface to reserve a given travel service having a first cost that exceeds the minimum travel value and a second cost that is less than the maximum purchase amount;

identifying a travel end date for the given travel service; and

preventing the user from reserving an additional travel service for a specified period of time of the travel end date, the preventing including disallowing the user to search for the additional travel service within the interactive graphical user interface.

18. A non-transitory computer-readable medium comprising instructions stored thereon that are executable by at least one processor to cause a computing device to perform operations comprising:

receiving travel information with a travel date for a user;

computing a subscription value as a function of a booking date and the travel date, the subscription value comprising an accumulated value portion and an amortized value portion, the accumulated value portion calculated by determining a time interval between the booking date and the travel date and accumulating the subscription value over the time interval;

determining a minimum travel value and a maximum purchase amount based on the subscription value;

training a machine-learning model to associate classifications with users based on travel services activities of the users and using a log odds model to estimate coefficients of the machine-learning model, wherein the travel services activities of the users used to train the machine-learning model comprise quantities of reservations made by the users, subscription durations of the users, distances to travel destinations for the users, margin amounts for the users, reservation frequencies of the users, and cancelation frequencies of cancelations by the users, and wherein the classifications represent activity levels for the users;

generating a classification for the user based on (i) the coefficients of the machine-learning model, as trained, and (ii) travel activities of the user;

selecting a subset of candidate travel services by filtering candidate travel services based on (i) the classification of the user generated by the machine-learning model, as trained, (ii) the travel date, (iii) the minimum travel value, and (iv) the maximum purchase amount;

receiving a request from the user via an interactive graphical user interface to reserve a given travel service having a first cost that exceeds the minimum travel value and a second cost that is less than the maximum purchase amount;

identifying a travel end date for the given travel service; and

preventing the user from reserving an additional travel service for a specified period of time of the travel end date, the preventing including disallowing the user to search for the additional travel service within the interactive graphical user interface.