Abstract: This disclosure includes methods for predicting demand based on the price of a time-expiring inventory. An online system provides a connection between a manager of a time-expiring inventory and a plurality of clients. The online system provides a listing for the manager's time-expiring inventory to clients on the online system. The manager specifies the price of the time-expiring inventory in the listing. A demand function predicts the demand for the time-expiring inventory based on features of the listing and the time-expiring inventory. The online system determines a likelihood of receiving a request for the time-expiring inventory from a client on the online system based on the predicted demand. The online system may use the determined likelihoods to provide to the manager information about how changes in the price of the listing are likely to affect the demand for the time-expiring inventory.

Abstract: Methods and systems for machine learning assisted search functions for unique accommodations founded in listing booking conversion are disclosed. In one embodiment, an online booking system models the conversion propensity of listings based on statistical relationships between features of previously received accommodation reservation requests and the booking of those reservation requests by guests. In particular, the system classifies reservation requests based on several features—a reservation request either possesses a feature or does not possess a feature. The conversion propensity of a listing for a particular request feature is modeled based on the relationship between the reservation requests that possess the feature and the reservation requests that are booked by a guest.

Abstract: Methods and systems for determining the preferences of hosts offering accommodations are disclosed. In one embodiment, an online booking system models the preferences of hosts based on statistical relationships between features of previously received accommodation reservation requests and the acceptance of those reservation requests by the hosts. In particular, the system classifies reservation requests based on several features—a reservation request either possesses a feature or does not possess a feature. The preference of a host for a particular request feature is modeled based on the relationship between the reservation requests that possess the feature and the reservation requests that are accepted by the host.

Abstract: Methods and systems for determining the preferences of hosts offering accommodations are disclosed. In one embodiment, an online booking system models the preferences of hosts based on statistical relationships between features of previously received accommodation reservation requests and the acceptance of those reservation requests by the hosts. In particular, the system classifies reservation requests based on several features—a reservation request either possesses a feature or does not possess a feature. The preference of a host for a particular request feature is modeled based on the relationship between the reservation requests that possess the feature and the reservation requests that are accepted by the host.

Abstract: Methods and systems for determining the preferences of hosts offering accommodations are disclosed. In one embodiment, an online booking system models the preferences of hosts based on statistical relationships between features of previously received accommodation reservation requests and the acceptance of those reservation requests by the hosts. In particular, the system classifies reservation requests based on several features—a reservation request either possesses a feature or does not possess a feature. The preference of a host for a particular request feature is modeled based on the relationship between the reservation requests that possess the feature and the reservation requests that are accepted by the host.

Abstract: Methods and systems for determining the preferences of hosts offering accommodations are disclosed. In one embodiment, an online booking system models the preferences of hosts based on statistical relationships between features of previously received accommodation reservation requests and the acceptance of those reservation requests by the hosts. In particular, the system classifies reservation requests based on several features—a reservation request either possesses a feature or does not possess a feature. The preference of a host for a particular request feature is modeled based on the relationship between the reservation requests that possess the feature and the reservation requests that are accepted by the host.

Abstract: Methods and systems for machine learning assisted search functions for unique accommodations founded in listing booking conversion are disclosed. In one embodiment, an online booking system models the conversion propensity of listings based on statistical relationships between features of previously received accommodation reservation requests and the booking of those reservation requests by guests. In particular, the system classifies reservation requests based on several features—a reservation request either possesses a feature or does not possess a feature. The conversion propensity of a listing for a particular request feature is modeled based on the relationship between the reservation requests that possess the feature and the reservation requests that are booked by a guest.

Abstract: This disclosure includes methods for predicting demand based on the price of a time-expiring inventory. An online system provides a connection between a manager of a time-expiring inventory and a plurality of clients. The online system provides a listing for the manager's time-expiring inventory to clients on the online system. The manager specifies the price of the time-expiring inventory in the listing. A demand function predicts the demand for the time-expiring inventory based on features of the listing and the time-expiring inventory. The online system determines a likelihood of receiving a request for the time-expiring inventory from a client on the online system based on the predicted demand. The online system may use the determined likelihoods to provide to the manager information about how changes in the price of the listing are likely to affect the demand for the time-expiring inventory.

Abstract: Methods and systems for determining the preferences of hosts offering accommodations are disclosed. In one embodiment, an online booking system models the preferences of hosts based on statistical relationships between features of previously received accommodation reservation requests and the acceptance of those reservation requests by the hosts. In particular, the system classifies reservation requests based on several features—a reservation request either possesses a feature or does not possess a feature. The preference of a host for a particular request feature is modeled based on the relationship between the reservation requests that possess the feature and the reservation requests that are accepted by the host.