Abstract: An online reservation system is configured to receive requests from a guest for searching property listings and to return property listings that satisfy the search criteria of the requests. The online reservation system uses a machine learning system to rank the property listings returned by the search. The machine learning system uses objective functions to determine parameters for each property listing and assign a ranking based on the parameters. A first objective function generates a parameter indicating an extent to which a property listing matches preferences of the guest, and is based on data about the guest's interactions with the reservation system. A second objective function generates another parameter indicating an extent to which the search request matches the preferences of the host associate with the property listing, and is based on data about the host's responses to reservation requests.

Abstract: A user is associated with initial search requests, and results that comprise attribute types indicative of a common relationship with other results. Each result has an attribute parameter for each attribute type. Search interaction data. Search interaction data comprises attribute parameter data and user interaction data for the search results. A machine learning algorithm is trained to analyze the search interaction data to recognize common relationships, and used to detect a common relationship between the respective attribute parameters for one of the attribute types for which the user interest data indicates interest. When a subsequent search request is received from the user, a user interest characteristic is computed for each result, based on similarity between the attribute preference data detected using the machine learning algorithm and the attribute parameter for the attribute type. The search results are presented to the user, sorted according to user interest characteristic.

Abstract: An online reservation system is configured to receive requests from a guest for searching property listings and to return property listings that satisfy the search criteria of the requests. The online reservation system also tracks interactions of the guest with the returned property listings in order to determine which of the property listings are of interest to the guest, and the system may determine one or more guest preference parameters indicative of the guest's preferences based on such interactions. Thereafter, when the guest submits another search request, the system sorts the search results based on the guest preference parameters so that the property listings deemed more likely to be of interest to the guest are ranked higher (e.g., listed first), thereby helping the guest to more quickly find property listings of interest within the search results.

Abstract: An online reservation system is configured to receive requests from a guest for searching property listings and to return property listings that satisfy the search criteria of the requests. The online reservation system uses a machine learning system to rank the property listings returned by the search. The machine learning system uses objective functions to determine parameters for each property listing and assign a ranking based on the parameters. A first objective function generates a parameter indicating an extent to which a property listing matches preferences of the guest, and is based on data about the guest's interactions with the reservation system. A second objective function generates another parameter indicating an extent to which the search request matches the preferences of the host associate with the property listing, and is based on data about the host's responses to reservation requests.

Abstract: An online reservation system is configured to receive requests from a guest for searching property listings and to return property listings that satisfy the search criteria of the requests. The online reservation system also tracks interactions of the guest with the returned property listings in order to determine which of the property listings are of interest to the guest, and the system may determine one or more guest preference parameters indicative of the guest's preferences based on such interactions. Thereafter, when the guest submits another search request, the system sorts the search results based on the guest preference parameters so that the property listings deemed more likely to be of interest to the guest are ranked higher (e.g., listed first), thereby helping the guest to more quickly find property listings of interest within the search results.