Airbnb, Inc.

Maxim Charkov

Spencer De Mars

Bar Ifrach

Kim Pham

Joseph Zadeh

Matt Weisinger

Nathan Blecharczyk

Riley Newman

Jan Overgoor

Surabhi Gupta

Tao Xu

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.

Automated determination of booking availability for user sourced accommodations

A computer implemented system and method for selecting and notifying operators of the option to enable a record activation feature for a short interval of time for the records they offer in a selected geographic area. Enabling record activation for a record indicates that the record may be booked without the operator's to manual approval of the transaction. Before selecting and notifying operators, a demand for database requests is predicted. Operators that are most likely to offer their record for record activation are identified. A quality score is determined for each identified record based on the likelihood that the record will get booked once the operator has programmatically enabled record activation. The records needed to fulfill the demand for database requests are selected based on their quality score and the operators of the selected records are notified of the option to enable record activation.

Automated database record activation using predictive modeling of database access

An online booking system allows users to creates, search, and book listings of goods or services. When a user searches for listings, the listings are ranked at least in part based on a location relevance score including at least one of a city relevance subscore, a neighborhood subscore, and a distance subscore. Generally, the city relevance subscore quantifies the probability that a searching user may have actually intended to look for listings in a city other than the city specified in a search query. Generally, the neighborhood relevance subscore quantifies the popularity of specific neighborhoods within a city as a replacement or addition to the distance subscore that determines a real world distance between a listing's real world location and a location specified in a search query.

Location based ranking of real world locations

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&#x2014;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.

Unique accommodation search improvement founded in listing booking conversion

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&#x2014;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.

Determining host preferences for accommodation listings

An online booking system allows users to search and book listings of goods or services. When a user searches for listings, the listings are ranked and scored based on a number of factors including the location and the price of the listing, the number and quality of reviews and the number of successful prior bookings. In some situations, the listing scores overly skew the top ranking results to a particular region. The listing scores may be modified to address this skewing of results. When diversity in search results is desired, the listing scores are modified such that the top ranking results that are located in a diverse set of regions. When granular relevance of search results is desired, the listing scores are modified such that the top ranking results are located in regions that are more relevant to the search than the region to which the results are skewed.

Re-ranking search results for location refining and diversity

A computer implemented system and method for selecting and notifying operators of the option to enable a record activation feature for a short interval of time for the records they offer in a selected geographic area. Enabling record activation for a record indicates that the record may be booked by a user without first requesting the operator to manually approve the transaction request and waiting for the operator's approval of the request. Before selecting and notifying operators, a demand for database requests is predicted. Operators that are most likely to offer their record for record activation are identified. A quality score is determined for each identified record based on the likelihood that the record will get booked once the operator has programmatically enabled record activation. The records needed to fulfill the demand for database requests are selected based on their quality score and the operators of the selected records are notified of the option to enable record activation.

An online booking system allows users to create, search, and book listings of goods or services. When a user searches for listings, the listings are ranked at least in part based on a location relevance score including at least one of a city relevance subscore, a neighborhood subscore, and a distance subscore. Generally, the city relevance subscore quantifies the probability that a searching user may have actually intended to look for listings in a city other than the city specified in a search query. Generally, the neighborhood relevance subscore quantifies the popularity of specific neighborhoods within a city as a replacement or addition to the distance subscore that determines a real world distance between a listing's real world location and a location specified in a search query.

Growing community of inventors

San Francisco, CA, United States of America

Maxim Charkov