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公开(公告)号：US10572833B2
公开(公告)日：2020-02-25
申请号：US16358393
申请日：2019-03-19
申请人： Airbnb, Inc.
发明人： Bar Ifrach , Spencer de Mars , Maxim Charkov
摘要： 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.
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2.发明授权公开(公告)号：US10528909B2
公开(公告)日：2020-01-07
申请号：US15482453
申请日：2017-04-07
申请人： Airbnb, Inc.
发明人： Spencer de Mars , Yangli Hector Yee , Peng Ye , Fenglin Liao , Li Zhang , Kim Pham , Julian Qian , Benjamin Yolken
摘要： This disclosure includes systems for regression-tree-modified feature vector machine learning models for utilization prediction in time-expiring inventory. An online computing system receives a feature vector for a listing and inputs the feature vector and modified feature vectors into a demand function to generate demand estimates. The system inputs the demand estimates into a likelihood model to generate a set of request likelihoods, each request likelihood representing a likelihood that the time-expiring inventory will receive a transaction request at each of a set of test price and test times to expiration. The system further trains a regression tree model based on a set of training data comprising each of the request likelihoods from the set and the test price and test time period to expiration used to generate the demand estimate that was used to generate the request likelihood.
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3.发明申请公开(公告)号：US20170308846A1
公开(公告)日：2017-10-26
申请号：US15482453
申请日：2017-04-07
申请人： Airbnb, Inc.
发明人： Spencer de Mars , Yangli Hector Yee , Peng Ye , Fenglin Liao , Li Zhang , Kim Pham , Julian Qian , Benjamin Yolken
摘要： This disclosure includes systems for regression-tree-modified feature vector machine learning models for utilization prediction in time-expiring inventory. An online computing system receives a feature vector for a listing and inputs the feature vector and modified feature vectors into a demand function to generate demand estimates. The system inputs the demand estimates into a likelihood model to generate a set of request likelihoods, each request likelihood representing a likelihood that the time-expiring inventory will receive a transaction request at each of a set of test price and test times to expiration. The system further trains a regression tree model based on a set of training data comprising each of the request likelihoods from the set and the test price and test time period to expiration used to generate the demand estimate that was used to generate the request likelihood.
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公开(公告)号：US10942931B2
公开(公告)日：2021-03-09
申请号：US16235551
申请日：2018-12-28
申请人： Airbnb, Inc.
发明人： Spencer de Mars , Kim Pham , Maxim Charkov
IPC分类号： G06F16/2457 , G06Q30/02 , G06Q50/14 , G06F16/248 , G06Q10/02
摘要： 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.
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5.发明申请公开(公告)号：US20200090116A1
公开(公告)日：2020-03-19
申请号：US16688569
申请日：2019-11-19
申请人： Airbnb, Inc.
发明人： Spencer de Mars , Yangli Hector Yee , Peng Ye , Fenglin Liao , Li Zhang , Kim Pham , Julian Qian , Benjamin Yolken
摘要： This disclosure includes systems for regression-tree-modified feature vector machine learning models for utilization prediction in time-expiring inventory. An online computing system receives a feature vector for a listing and inputs the feature vector and modified feature vectors into a demand function to generate demand estimates. The system inputs the demand estimates into a likelihood model to generate a set of request likelihoods, each request likelihood representing a likelihood that the time-expiring inventory will receive a transaction request at each of a set of test price and test times to expiration. The system further trains a regression tree model based on a set of training data comprising each of the request likelihoods from the set and the test price and test time period to expiration used to generate the demand estimate that was used to generate the request likelihood.
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公开(公告)号：US20190138529A1
公开(公告)日：2019-05-09
申请号：US16235551
申请日：2018-12-28
申请人： Airbnb, Inc.
发明人： Spencer de Mars , Kim Pham , Maxim Charkov
IPC分类号： G06F16/2457 , G06F16/248 , G06Q50/14 , G06Q30/02 , G06Q10/02
摘要： 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.
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公开(公告)号：US20160098649A1
公开(公告)日：2016-04-07
申请号：US14505455
申请日：2014-10-02
申请人： Airbnb, Inc.
发明人： Bar Ifrach , Spencer de Mars , Maxim Charkov
CPC分类号： G06Q10/02 , G06F17/18 , G06Q50/12 , G06Q50/14 , G06Q50/163
摘要： 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.
摘要翻译： 公开了确定提供住宿的主机偏好的方法和系统。 在一个实施例中，在线预订系统基于先前接收到的住宿预约请求的特征与主机对这些预约请求的接受之间的统计关系来模拟主机的偏好。 特别地，系统基于若干特征对预留请求进行分类 - 预留请求具有特征或不具有特征。 基于具有该特征的预留请求与主机所接受的预留请求之间的关系来建模用于特定请求特征的主机的偏好。
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公开(公告)号：US10607160B2
公开(公告)日：2020-03-31
申请号：US15452307
申请日：2017-03-07
申请人： Airbnb, Inc.
发明人： Tao Xu , Bar Ifrach , Spencer de Mars , Maxim Charkov
摘要： 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.
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公开(公告)号：US10354206B2
公开(公告)日：2019-07-16
申请号：US14505455
申请日：2014-10-02
申请人： Airbnb, Inc.
发明人： Bar Ifrach , Spencer de Mars , Maxim Charkov
摘要： 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.
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公开(公告)号：US20190213507A1
公开(公告)日：2019-07-11
申请号：US16358393
申请日：2019-03-19
申请人： Airbnb, Inc.
发明人： Bar Ifrach , Spencer de Mars , Maxim Charkov
摘要： 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.
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