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公开(公告)号：US11308564B2
公开(公告)日：2022-04-19
申请号：US16214359
申请日：2018-12-10
申请人： Airbnb, Inc.
摘要： Systems and methods are provided for extracting a plurality of features for a listing from a datastore comprising a plurality of listings and a plurality of features for each of the plurality of listings, determining a cluster of similar listings to the listing and generating a set of cluster features for the cluster of similar listings, analyzing the set of cluster features for the cluster of similar listings based on a booking price, using a first trained machine learning model to determine a cluster-level probability of booking the listing on the given date, analyzing the plurality of features for the listing using the booking price, using a second trained machine learning model to determine a listing-level probability of booking the listing on the given date, and generating a final probability of booking by combining the cluster-level probability of booking and the listing-level probability of booking.
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公开(公告)号：US20230306541A1
公开(公告)日：2023-09-28
申请号：US18326311
申请日：2023-05-31
申请人： Airbnb, Inc.
发明人： Siarhei Bykau , Peng Ye
IPC分类号： G06Q50/16 , G06Q30/0201 , G06Q30/0601 , G06F16/29 , G06F16/583 , G06V10/75 , G06Q10/02
CPC分类号： G06Q50/167 , G06Q30/0201 , G06Q30/0623 , G06F16/29 , G06F16/583 , G06V10/757 , G06Q10/02
摘要： Two sets of data, each containing property listings, are obtained from two discrete merchant platforms. Each property listing in a set of data of a first merchant is sequentially paired with each of the property listings in a set of data of a second merchant. For each pair, each image of the property listing of the first merchant is compared to each image of the property listing of the second merchant, and images of statistically sufficient similarity are identified. The similarity of images, and in particular, of similar images likely to be rooms of the property, are considered in a determination of whether the product listings of the first and second merchant are for the same cross-listed product.
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3.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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4.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：US20240362732A1
公开(公告)日：2024-10-31
申请号：US18768824
申请日：2024-07-10
申请人： Airbnb, Inc.
发明人： Siarhei Bykau , Peng Ye
IPC分类号： G06Q50/16 , G06F16/29 , G06F16/583 , G06Q10/02 , G06Q30/0201 , G06Q30/0601 , G06V10/75
CPC分类号： G06Q50/167 , G06F16/29 , G06F16/583 , G06Q10/02 , G06Q30/0201 , G06Q30/0623 , G06V10/757
摘要： Two sets of data, each containing property listings, are obtained from two discrete merchant platforms. Each property listing in a set of data of a first merchant is sequentially paired with each of the property listings in a set of data of a second merchant. For each pair, each image of the property listing of the first merchant is compared to each image of the property listing of the second merchant, and images of statistically sufficient similarity are identified. The similarity of images, and in particular, of similar images likely to be rooms of the property, are considered in a determination of whether the product listings of the first and second merchant are for the same cross-listed product.
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公开(公告)号：US12067634B2
公开(公告)日：2024-08-20
申请号：US18326311
申请日：2023-05-31
申请人： Airbnb, Inc.
发明人： Siarhei Bykau , Peng Ye
IPC分类号： G06Q50/16 , G06F16/29 , G06F16/583 , G06Q10/02 , G06Q30/0201 , G06Q30/0601 , G06V10/75
CPC分类号： G06Q50/167 , G06F16/29 , G06F16/583 , G06Q10/02 , G06Q30/0201 , G06Q30/0623 , G06V10/757
摘要： Two sets of data, each containing property listings, are obtained from two discrete merchant platforms. Each property listing in a set of data of a first merchant is sequentially paired with each of the property listings in a set of data of a second merchant. For each pair, each image of the property listing of the first merchant is compared to each image of the property listing of the second merchant, and images of statistically sufficient similarity are identified. The similarity of images, and in particular, of similar images likely to be rooms of the property, are considered in a determination of whether the product listings of the first and second merchant are for the same cross-listed product.
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7.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：US11869106B1
公开(公告)日：2024-01-09
申请号：US16578116
申请日：2019-09-20
申请人： Airbnb, Inc.
发明人： Siarhei Bykau , Peng Ye
IPC分类号： G06F16/29 , G06F16/583 , G06Q10/02 , G06Q50/16 , G06V10/75 , G06Q30/0201 , G06Q30/0601
CPC分类号： G06Q50/167 , G06F16/29 , G06F16/583 , G06Q10/02 , G06Q30/0201 , G06Q30/0623 , G06V10/757
摘要： Two sets of data, each containing property listings, are obtained from two discrete merchant platforms. Each property listing in a set of data of a first merchant is sequentially paired with each of the property listings in a set of data of a second merchant. For each pair, each image of the property listing of the first merchant is compared to each image of the property listing of the second merchant, and images of statistically sufficient similarity are identified. The similarity of images, and in particular, of similar images likely to be rooms of the property, are considered in a determination of whether the product listings of the first and second merchant are for the same cross-listed product.
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公开(公告)号：US20200184580A1
公开(公告)日：2020-06-11
申请号：US16214359
申请日：2018-12-10
申请人： Airbnb, Inc.
摘要： Systems and methods are provided for extracting a plurality of features for a listing from a datastore comprising a plurality of listings and a plurality of features for each of the plurality of listings, determining a cluster of similar listings to the listing and generating a set of cluster features for the cluster of similar listings, analyzing the set of cluster features for the cluster of similar listings based on a booking price, using a first trained machine learning model to determine a cluster-level probability of booking the listing on the given date, analyzing the plurality of features for the listing using the booking price, using a second trained machine learning model to determine a listing-level probability of booking the listing on the given date, and generating a final probability of booking by combining the cluster-level probability of booking and the listing-level probability of booking.
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公开(公告)号：US20180018683A1
公开(公告)日：2018-01-18
申请号：US15213062
申请日：2016-07-18
申请人： Airbnb, Inc.
发明人： Yangli Hector Yee , Li Zhang , Carla Pellicano , Peng Ye
IPC分类号： G06Q30/02
CPC分类号： G06Q30/0202 , G06Q10/02 , G06Q50/14
摘要： 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 and is presented with price tips generated by the online system. A demand function predicts the demand for the time-expiring inventory based on features of the listing and the time-expiring inventory. A manager option function predicts the likelihood of acceptance of a price tip by the manager. The online system uses the demand function and the manager option function to create a Monte Carlo pricing model to provide to the manager price tips for the listing.
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