Abstract: Features are disclosed for providing items from an item store to an item store consumer. An item store organizer can request an item store from an item redemption system based on item store parameters. The item store organizer can receive a single use token from the item redemption system to provide initial access to the item store. The item redemption system can validate the item store consumer based on the single use token. The item redemption system can further validate redemption requests from the item store consumer based on the item store parameters. The item redemption system can fulfill the redemption request based on payment information of the item store organizer and shipping information of the item store consumer. The item redemption system can transmit redemption information to the item store organizer based on fulfilling the redemption request.

Abstract: Devices and techniques are generally described for dynamic policy determination using machine learning. In some examples, first goal data may be received. A first machine learning model may generate a first computer-executable policy based at least in part on the first goal data. In some examples, a first search query may be received. In various examples, the first search query may be modified by the first computer-executable policy to generate a modified search query. In some examples, first feedback data related to user interaction with search results of the modified search query may be determined. In various examples, at least one parameter of the first machine learning model may be updated based at least in part on the first feedback data to generate a second machine learning model.

Abstract: Features are disclosed for providing items from an item store to an item store consumer. An item store organizer can request an item store from an item redemption system based on item store parameters. The item store organizer can receive a single use token from the item redemption system to provide initial access to the item store. The item redemption system can validate the item store consumer based on the single use token. The item redemption system can further validate redemption requests from the item store consumer based on the item store parameters. The item redemption system can fulfill the redemption request based on payment information of the item store organizer and shipping information of the item store consumer. The item redemption system can transmit redemption information to the item store organizer based on fulfilling the redemption request.

Abstract: Query-correction pairs can be extracted from search log data. Each query-correction pair can include an original query and a follow-up query, where the follow-up query meets one or more criteria for being identified as a correction of the original query, such as an indication of user input indicating the follow-up query is a correction for the original query. The query-correction pairs can be segmented to identify bi-phrases in the query-correction pairs. Probabilities of corrections between the bi-phrases can be estimated based on frequencies of matches in the query-correction pairs. Identifications of the bi-phrases and representations of the probabilities of those bi-phrases can be stored in a probabilistic model data structure.