Automated determination of booking availability for user sourced accommodations

• US 10,467,553 B2
• Filed: 03/13/2013
• Issued: 11/05/2019
• Est. Priority Date: 03/13/2013
• Status: Active Grant

• Current Assignee: AirBNB Incorporated
• Original Assignee: AirBNB Incorporated
• Inventors: Blecharczyk, Nathan, Charkov, Maxim, Weisinger, Matt, Newman, Riley, Zadeh, Joseph
• Primary Examiner(s): Epstein, Brian M
• Assistant Examiner(s): Zeroual, Omar
• Application Number:
  US13/802,025
• Publication Number:
  US 20140278591A1
• Time in Patent Office: 2,428 Days
• Field of Search: 705 5
• CPC Class Codes: G06Q 10/02 Reservations, e.g. for tickets, services or events
• CPC Class Codes: G06Q 10/06 Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
• CPC Class Codes: G06Q 50/14 Travel agencies

Abstract

Methods and systems for updating a calendar entry for an accommodation listing are disclosed. In one embodiment, the method comprises generating an availability model and an acceptance model for an accommodation listing in an accommodation reservation system and determining based on those models the probability that the accommodation listing would be able to be booked. Furthermore, the result of an accommodation search query can be filtered and/or sorted using the determined probability of booking.

Claims

1. A computer implemented method, comprising:

identifying a plurality of past booking requests for accommodations listed by hosts on a reservation platform, each of the past booking requests having a plurality of features including (i) an identity of a host of the accommodation being requested, (ii) a date range associated with the booking request, (iii) a geographic region associated with the booking request, and (iv) whether the booking request was accepted or rejected by the host of the accommodation;

training a predictive computer model based on values of the plurality of features of the booking requests, the predictive computer model comprising, for each of the accommodations, a probability function that represents a relationship between at least one characteristic of a given day of the year and availability of the accommodation on the given day;

receiving, by a computer, a search query from a guest for accommodation, the search query comprising a geographical location and a requested date range;

identifying, by the computer, a set of candidate accommodations in the geographical location that are not booked during the requested date range, each of the candidate accommodations associated with an availability calendar that is maintained by the host of the accommodation and indicates that the candidate accommodation is not booked during the requested date range;

providing a user interface to the guest comprising identifiers of the set of candidate accommodations, the identifiers being sorted according to a default parameter;

detecting a selection by the guest of an option to re-sort the identifiers according to an availability criterion;

responsive to detecting the selection by the guest of the option, for each of the candidate accommodations, calculating, by the computer applying the predictive computer model to the requested date range, a predicted availability that indicates a likelihood that the candidate accommodation that is not booked during the requested date range according to the availability calendar is actually available for booking during the requested date range;

ranking, by the computer, the candidate accommodations based at least on their respective predicted availabilities; and

updating the user interface to provide, to the guest, the identifiers as re-sorted according to the ranking.

2. The computer implemented method of claim 1, wherein the past booking requests were made for a number of days in the past.

3. The computer implemented method of claim 1, wherein the past booking requests were made at most a threshold number of days in the past.

4. The computer implemented method of claim 1, wherein calculating the predicted availability is based on a day of a week the accommodation is requested for.

5. The computer implemented method of claim 1, wherein calculating the predicted availability is based on a month the accommodation is requested for.

6. The computer implemented method of claim 1, wherein calculating the predicted availability is based on whether a day the accommodation was requested for is a holiday.

7. The method of claim 1, further comprising for each of the candidate accommodations, determining, by the computer, a probability of booking acceptance that indicates a likelihood that a booking request for the candidate accommodation will be accepted by the host of the accommodation in response to a booking request received from the guest, the probability of booking acceptance for the candidate accommodation being based upon the predicted availability for that candidate accommodation.

8. The method of claim 7, wherein ranking, by the computer, the candidate accommodations is further based on the probabilities of booking acceptance calculated for the candidate accommodations.

9. The method of claim 1, wherein ranking, by the computer, the candidate accommodations is further based on price, host rating, and distance from preferred location.

10. An accommodation reservation system, comprising:

a computer processor;

a search module executed by the processor and configured to;

identify a plurality of past booking requests for accommodations listed by hosts on a reservation platform, each of the past booking requests having a plurality of features including (i) an identity of a host of the accommodation being requested, (ii) a date range associated with the booking request, (iii) a geographic region associated with the booking request, and (iv) whether the booking request was accepted or rejected by the host of the accommodation;

train a predictive computer model based on values of the plurality of features of the booking requests, the predictive computer model comprising, for each of the accommodations, a probability function that represents a relationship between at least one characteristic of a given day of the year and availability of the accommodation on the given day;

receive a search query from a guest, the search query comprising a geographical location and a requested date range;

identify a set of candidate accommodations that are not booked during the requested date range based on the search query, each of the candidate accommodations associated with an availability calendar that is maintained by the host of the accommodation and indicates that the candidate accommodation is not booked during the requested date range;

provide a user interface to the guest comprising identifiers of the set of candidate accommodations, the identifiers being sorted according to a default parameter;

detect a selection by the guest of an option to re-sort the identifiers according to an availability criterion;

responsive to detecting the selection by the guest of the option, for each of the candidate accommodations, calculating, by an availability module applying the predictive computer model to the requested date range, a predicted availability that indicates a likelihood that the candidate accommodation that is not booked during the requested date range according to the availability calendar is actually available for booking during the requested date range;

rank the candidate accommodations based at least on their respective predicted availabilities;

update the user interface to provide, to the guest, the identifiers as re-sorted according to the ranking.

11. The accommodation reservation system of claim 10, wherein the past booking requests were made for a number of days in the past.

12. The accommodation reservation system of claim 10, wherein calculating the predicted availability is based at least on one selected from the group consisting of a day of a week the accommodation is requested for, a month the accommodation is requested for, and whether the day the accommodation was requested for is a holiday.

13. The accommodation reservation system of claim 10, wherein the booking requests were made at most a threshold number of days in the past.

14. The accommodation reservation system of claim 10, wherein calculating the predicted availability is based on a day of a week the accommodation is requested for.

15. The accommodation reservation system of claim 10, wherein calculating the predicted availability is based on a month the accommodation is requested for.

16. A computer program product comprising a non-transitory computer-readable storage medium containing computer program code, the computer program code when executed by a processor causes the processor to:

identify a plurality of past booking requests for accommodations listed by hosts on a reservation platform, each of the past booking requests having a plurality of features including (i) an identity of a host of the accommodation being requested, (ii) a date range associated with the booking request, (iii) a geographic region associated with the booking request, and (iv) whether the booking request was accepted or rejected by the host of the accommodation;

train a predictive computer model based on values of the plurality of features of the booking requests, the predictive computer model comprising, for each of the accommodations, a probability function that represents a relationship between at least one characteristic of a given day of the year and availability of the accommodation on the given day;

receive a search query from a guest, the search query comprising a geographical location and a requested date range;

identify a set of candidate accommodations that are not booked during the requested date range based on the search query, each of the candidate accommodations associated with an availability calendar that is maintained by the host of the accommodation and indicates that the candidate accommodation is not booked during the requested date range;

provide a user interface to the guest comprising identifiers of the set of candidate accommodations, the identifiers being sorted according to a default parameter;

detect a selection by the guest of an option to re-sort the identifiers according to an availability criterion;

responsive to detecting the selection by the guest of the option, for each of the candidate accommodations, calculating, by an availability module applying the predictive computer model to the requested date range, a predicted availability that indicates a likelihood that the candidate accommodation that is not booked during the requested date range according to the availability calendar is actually available for booking during the requested date range;

rank the candidate accommodations based at least on their respective predicted availabilities;

update the user interface to provide, to the guest, the identifiers as re-sorted according to the ranking.

17. The computer program product of claim 16, wherein the past booking requests were made at most a threshold number of days in the past.

18. The computer program product of claim 16, wherein calculating the predicted availability is based at least on one selected from the group consisting of a day of a week the accommodation is requested for, a month the accommodation is requested for, and whether the day the accommodation was requested for is a holiday.

19. The computer program product of claim 16, wherein calculating the predicted availability is based on at least one of a day of a week or a month the accommodation is requested for.