Abstract: Virtualization methods and systems for optimizing the availability of items in an inventory of items in a reservation system, wherein the items are classified into item types and an item type is defined by a requestable set of at least one characteristic. Reservations may be received for a set of at least one characteristic that is a subset of an item type. After a reservation has been accepted, all the availabilities of the requestable sets of at least one characteristic in the inventory are updated. The reservation system may be a hotel reservation system and item types may be hotel room types or other bookable products. The reservation system may be a flight reservation system and item types may be bookable places on flights.

Abstract: Computer-implemented methods of providing personalized recommendations to a user of items available in an online system, and related systems. First-level features including context features are computed based upon context data. A first-level machine learning model is then evaluated using the first-level features to generate predictions of user behavior in relation to a plurality of individual items available via the online system. A list of proposed item recommendations is constructed based upon the predictions. Second-level features are computed based upon the context data and list features based upon the list of proposed item recommendations and the corresponding predictions generated by the first-level machine learning model. A second-level machine learning model is evaluated using the second-level features to generate a prediction of user behavior in relation to the list of proposed item recommendations.

Abstract: Virtualization methods and systems for optimizing the availability of items in an inventory of items in a reservation system. The items are classified into item types and an item type is defined by a requestable set of at least one characteristic. Reservations may be received for a set of at least one characteristic that is a subset of an item type. After a reservation has been accepted, all the availabilities of the requestable sets of at least one characteristic in the inventory are updated. The reservation system may be a hotel reservation system and item types may be hotel room types or other bookable products. The reservation system may be a flight reservation system and item types may be bookable places on flights.

Abstract: Virtualization methods and systems for optimizing the availability of items in an inventory of items in a reservation system, wherein the items are classified into item types and an item type is defined by a requestable set of at least one characteristic. Reservations may be received for a set of at least one characteristic that is a subset of an item type. After a reservation has been accepted, all the availabilities of the requestable sets of at least one characteristic in the inventory are updated. The reservation system may be a hotel reservation system and item types may be hotel room types or other bookable products. The reservation system may be a flight reservation system and item types may be bookable places on flights.

Abstract: Computer-implemented methods of providing personalized recommendations to a user of items available in an online system, and related systems. First-level features including context features are computed based upon context data. A first-level machine learning model is then evaluated using the first-level features to generate predictions of user behavior in relation to a plurality of individual items available via the online system. A list of proposed item recommendations is constructed based upon the predictions. Second-level features are computed based upon the context data and list features based upon the list of proposed item recommendations and the corresponding predictions generated by the first-level machine learning model. A second-level machine learning model is evaluated using the second-level features to generate a prediction of user behavior in relation to the list of proposed item recommendations.

Abstract: Virtualization methods and systems for optimizing the availability of items in an inventory of items in a reservation system. The items are classified into item types and an item type is defined by a requestable set of at least one characteristic. Reservations may be received for a set of at least one characteristic that is a subset of an item type. After a reservation has been accepted, all the availabilities of the requestable sets of at least one characteristic in the inventory are updated. The reservation system may be a hotel reservation system and item types may be hotel room types or other bookable products. The reservation system may be a flight reservation system and item types may be bookable places on flights.

Abstract: Systems, methods, and computer program products for mining search query logs. A data warehousing system includes a query database that stores data relating to search queries, a reservation history database that stores data relating to booked products, and a data warehousing application that extracts and processes the search query and booking data from the query and reservation history databases to produce statistical data. The data warehousing application generates historical query, booking, and specific flight booking pickup curves based on the extracted statistical data. A weighted average of the historical query and booking pickup curves is determined that provides a best fit with the flight specific pickup curve. A weighting factor that produced the best fit is then used to forecast demand for future flights.

Abstract: Systems, methods, and computer program products for allotting capacity from a network to a plurality of group requests. Each request defines a number of units of capacity and a node pair that includes an origin node and a destination node. An allotment module identifies one or more routes connecting the node pair, and defines a plurality of selector functions. Each function identifies a plurality of request-route pairs that are satisfied without exceeding an available capacity of any of the directional links in the network. The allotment module determines a value that would be generated by allotting, for each request-route pair identified by the selector function, the number of units of capacity defined by the respective request from the respective route. The allotment module may then rank the selector functions based on the values, and allot the capacity to the requests identified by the highest ranked selector function.

Abstract: A revenue management system comprising a first revenue management route which includes a forecast module and an optimization module and which calculates expected revenue for sales of inventor items based on historical data; a second revenue management route which is selected if the confidence in the forecasting in the first route is below a predetermined value.

Abstract: A revenue management system comprising a first revenue management route which includes a forecast module and an optimization module and which calculates expected revenue for sales of inventory items based on historical data; a second revenue management route which is selected if the confidence in the forecasting in the first route is below a predetermined value.