Abstract: Disclosed are methods, systems, and computer-readable medium to perform operations including: receiving a query specifying one or more criteria for a travel reservation, transmitting, over a network, one or more requests for live travel data satisfying the one or more criteria, determining one or more query results that satisfy the one or more criteria, retrieving cached travel data for at least one of the one or more query results, and while responses to the one or more requests for live travel data are still being received over the network, generating, using a prediction engine, live travel data predictions for the one or more query results based at least in part on the cached travel data.

Abstract: Approaches to improving mixture queries and booking split stays are described herein. According to some aspects, a user utilizes an exploration tool to search for available lodging. A mixture query is generated based on the stay information received from the user and potential split stays are identified and presented to the user. In some aspects, the split stays are identified based on user-specified split stay criteria, search results are obtained from a local database of “live” pricing and availability information, and a selected split stay option is booked on behalf of the user. Example split stay criteria includes criteria pertaining to an amenity or characteristic of the available lodgings or the available rooms.

Abstract: Approaches to improving mixture queries and booking split stays are described herein. According to some aspects, a user utilizes an exploration tool to search for available lodging. A mixture query is generated based on the stay information received from the user and potential split stays are identified and presented to the user. In some aspects, the split stays are identified based on user-specified split stay criteria, search results are obtained from a local database of “live” pricing and availability information, and a selected split stay option is booked on behalf of the user. Example split stay criteria includes criteria pertaining to an amenity or characteristic of the available lodgings or the available rooms.

Abstract: Systems, methods, and computer-program products for online booking of lodging location reservations include receiving desired reservation information from a user; updating information stored in cache using a price and availability predictive machine learning model using the received desired reservation information; performing a search in the cache for solutions to the received desired reservation information; constructing all possible solutions available in the cache that satisfy the received desired reservation information, including splits in stays between more than one lodging locations; determining a score for each solution using a scoring machine learning model that considers the user's preferences; identifying a subset of solutions based on the score of each solution; performing live pricing and availability verification for the subset of solutions by querying a provider corresponding to each of the subset of solutions; and presenting the subset of solutions to the user with the verified pricing and ava

Abstract: Disclosed are methods, systems, and computer-readable medium to perform operations including: receiving a query specifying one or more criteria for a travel reservation, transmitting, over a network, one or more requests for live travel data satisfying the one or more criteria, determining one or more query results that satisfy the one or more criteria, retrieving cached travel data for at least one of the one or more query results, and while responses to the one or more requests for live travel data are still being received over the network, generating, using a prediction engine, live travel data predictions for the one or more query results based at least in part on the cached travel data.

Abstract: Approaches to improving mixture queries and booking split stays are described herein. According to some aspects, a user utilizes an exploration tool to search for available lodging. A mixture query is generated based on the stay information received from the user and potential split stays are identified and presented to the user. In some aspects, the split stays are identified based on user-specified split stay criteria, search results are obtained from a local database of “live” pricing and availability information, and a selected split stay option is booked on behalf of the user. Example split stay criteria includes criteria pertaining to an amenity or characteristic of the available lodgings or the available rooms.

Abstract: Approaches to improving mixture queries and booking split stays are described herein. According to some aspects, a user utilizes an exploration tool to search for available lodging. A mixture query is generated based on the stay information received from the user and potential split stays are identified and presented to the user. In some aspects, the split stays are identified based on user-specified split stay criteria, search results are obtained from a local database of “live” pricing and availability information, and a selected split stay option is booked on behalf of the user. Example split stay criteria includes criteria pertaining to an amenity or characteristic of the available lodgings or the available rooms.

Abstract: A method and system for ascribing a reputation of an entity as a rater of other entities is provided. A first rating indicative of a rating of a rated entity by the first entity, and one or more second ratings, each second rating indicative of a rating of the rated entity provided by another entity, are provided. The second ratings are combined to produce a first combined rating. The first rating is compared to the first combined rating to produce a first rating predictability of the first rating, the first rating predictability being a negative function of a magnitude of a difference between the first rating and the first combined rating. A resulting rater reputation is generated based at least in part on the first rating predictability.

Abstract: Agent-mediated commerce method and system, and agents for use therein. Seller agents may offer services at prices that vary over time, based on past experiences. Buyer agents may be configured by their users according to time and constraints, budget and the importance of a specific task. Buyer agents try, probabilistically, to maximize their owners' utilities (in part, by estimating the expected performance of each seller based on the reputation of that seller in the relevant marketplace. Buying agents may reveal only their time constraints and descriptions of the tasks (services) desired to the sellers. Seller agents bid for the offered tasks and base their bids at least partly on their owners' reputations, their time availability, the difficulty of the task and the current demand on the marketplace. Seller reputations are updated in a collaborative fashion based on seller performance. Seller agents employ dynamic pricing mechanisms, including specifically profit maximizing reputation followers.