Abstract: Disclosed are systems, methods, and non-transitory computer-readable media for message timing optimization. A message timing optimization system determines optimized times to transmit messages to users based on historical transaction data. For example, the message timing optimization system may determine an optimal time to transmit a recommendation message to a user to perform a subsequent, such as repurchasing an item, refreshing a password, and the like. The message timing optimization system uses historical transaction data describing previous transactions performed by the user and/or other users to determine probability values indicating the likelihood that a user will perform a subsequent action at various time periods after the user performed an initial transaction.

Abstract: Disclosed are systems, methods, and non-transitory computer-readable media for message timing optimization. A message timing optimization system determines optimized times to transmit messages to users based on historical transaction data. For example, the message timing optimization system may determine an optimal time to transmit a recommendation message to a user to perform a subsequent, such as repurchasing an item, refreshing a password, and the like. The message timing optimization system uses historical transaction data describing previous transactions performed by the user and/or other users to determine probability values indicating the likelihood that a user will perform a subsequent action at various time periods after the user performed an initial transaction.

Abstract: A system for distributing items is provided. Requests from first users for items available from second users are received. The first users are associated with different shipping addresses. Clusters are identified from the requests. The clusters have items with similar characteristics and are requested by different first users. The clusters are then matched to individuals of the second users. First instructions for the individuals are generated that indicate a single shipment having items purchased by the first users are to be sent to a processor. Second instructions are generated for the processor. The second instructions include sorting instructions to sort the items from shipments received from the individuals. The second instructions include address instructions to determine shipping addresses for the first users. The second instructions include packaging instructions for packaging the sorted items according to the shipping addresses such that a single package is sent to individual first users.

Abstract: A system may receive information representing a first item and an item price of the first item from a first user device operated by a buyer. The first item may be available for sale by a merchant of a brick-and-mortar store. A plurality of sellers may be identified, the plurality of sellers may list a second item as being available for sale at the calculated reduced item price in an item listing for the second item. Moreover, the second item may be similar to the first item. A reduced item price may be calculated based on the item price of the first item received from the user device, the reduced item price representing a price lower than the item price of the first item. The item listing for the second item available for sale may be presented to the first user device operated by the buyer.

Abstract: Disclosed are systems, methods, and non-transitory computer-readable media for message timing optimization. A message timing optimization system determines optimized times to transmit messages to users based on historical transaction data. For example, the message timing optimization system may determine an optimal time to transmit a recommendation message to a user to perform a subsequent, such as repurchasing an item, refreshing a password, and the like. The message timing optimization system uses historical transaction data describing previous transactions performed by the user and/or other users to determine probability values indicating the likelihood that a user will perform a subsequent action at various time periods after the user performed an initial transaction.

Abstract: A system may receive information representing a first item and an item price of the first item from a first user device operated by a buyer. The first item may be available for sale by a merchant of a brick-and-mortar store. A plurality of sellers may be identified, the plurality of sellers may list a second item as being available for sale at the calculated reduced item price in an item listing for the second item. Moreover, the second item may be similar to the first item. A reduced item price may be calculated based on the item price of the first item received from the user device, the reduced item price representing a price lower than the item price of the first item. The item listing for the second item available for sale may be presented to the first user device operated by the buyer.

Abstract: Automatic generation of individual item listings from a bulk listing is described. Bulk listing data associated with a bulk listing is obtained from a storage device associated with a listing system. The bulk listing data is processed to extract individual item information for individual items of the bulk listing. A plurality of individual item listings for the individual items of the bulk listing are automatically generated by populating the individual item listings with the respective individual item information extracted from the bulk listing data. In one or more implementations, relisting predictions for the individual items of the bulk listing are automatically generated and displayed in a graphical user interface.

Abstract: A system may receive information representing a first item and an item price of the first item from a first user device operated by a buyer. The first item may be available for sale by a merchant of a brick-and-mortar store. A plurality of sellers may be identified, the plurality of sellers may list a second item as being available for sale at the calculated reduced item price in an item listing for the second item. Moreover, the second item may be similar to the first item. A reduced item price may be calculated based on the item price of the first item received from the user device, the reduced item price representing a price lower than the item price of the first item. The item listing for the second item available for sale may be presented to the first user device operated by the buyer.

Abstract: Systems, devices, media and methods are presented for generalized a personalized product review sort within an online marketplace. In one example, a system receives from a client device associated with a first user, a request to view an online listing. The system accesses a set of reviews associated with an online listing. The system determines the relevance of each review in the set of reviews based on a correlation between a first user and a second user. The system determines an adjusted rating associated with the online listing and presents the adjusted rating in a graphical user interface of the client device.

Abstract: A system may receive information representing a first item and an item price of the first item from a first user device operated by a buyer. The first item may be available for sale by a merchant of a brick-and-mortar store. A plurality of sellers may be identified, the plurality of sellers may list a second item as being available for sale at the calculated reduced item price in an item listing for the second item. Moreover, the second item may be similar to the first item. A reduced item price may be calculated based on the item price of the first item received from the user device, the reduced item price representing a price lower than the item price of the first item. The item listing for the second item available for sale may be presented to the first user device operated by the buyer.

Abstract: A system may receive information representing a first item and an item price of the first item from a first user device operated by a buyer. The first item may be available for sale by a merchant of a brick-and-mortar store. A plurality of sellers may be identified, the plurality of sellers may list a second item as being available for sale at the calculated reduced item price in an item listing for the second item. Moreover, the second item may be similar to the first item. A reduced item price may be calculated based on the item price of the first item received from the user device, the reduced item price representing a price lower than the item price of the first item. The item listing for the second item available for sale may be presented to the first user device operated by the buyer.

Abstract: Various embodiments described herein enable a machine to identify electronic transactions involving a multi-directional electronic transaction arrangement on an electronic transaction system, such as an e-commerce website or platform that facilitates an online marketplace. Additionally, some embodiments identify multi-directional electronic transaction arrangements that facilitate multi-directional bartering between two or more e-commerce users, where the bartering may between multiple selling users not in a bartering chain or cycle.

Abstract: Systems and methods to predict bidding behavior are described. The system identifies a listing that includes listing information that describes an item that is being auctioned on a network-based marketplace. The system further identifies bid classification information based on the number of bids received for the item. Finally, the system predicts whether no more bids are expected to be received for the item based on the classification information.

Abstract: A system to generate an index for a closest match search is described. The system receives a corpus of information that includes member information. The system parses the member information to generate signatures for each of the members and stores the signatures in the index. The signatures are unique to the members. Accordingly, the signatures signify the respective members. The system subsequently utilizes the index to identify input information that matches signatures in the index to identify a closest match of the input information to one or more members in the corpus information.

Abstract: A system may receive information representing a first item and an item price of the first item from a first user device operated by a buyer. The first item may be available for sale by a merchant of a brick-and-mortar store. A plurality of sellers may be identified, the plurality of sellers may list a second item as being available for sale at the calculated reduced item price in an item listing for the second item. Moreover, the second item may be similar to the first item. A reduced item price may be calculated based on the item price of the first item received from the user device, the reduced item price representing a price lower than the item price of the first item. The item listing for the second item available for sale may be presented to the first user device operated by the buyer.

Abstract: A system to generate an index for a closest match search is described. The system receives a corpus of information that includes member information. The system parses the member information to generate signatures for each of the members and stores the signatures in the index. The signatures are unique to the members. Accordingly, the signatures signify the respective members. The system subsequently utilizes the index to identify input information that matches signatures in the index to identify a closest match of the input information to one or more members in the corpus of information.

Abstract: Systems and methods to predict bidding behaviour are described. The system identifies a listing that includes listing information that describes an item that is being auctioned on a network-based marketplace. The system further identifies bid classification information based on the number of bids received for the item. Finally, the system predicts whether no more bids are expected to be received for the item based on the classification information.

Abstract: In an example embodiment, e-commerce listings from a database are ranked into a first ranking in accordance with a first ranking scheme. Then a location of a potential buyer is identified. One or more of the listings are identified as may ship listings that a corresponding seller has indicated as pertaining to a good or service that may ship to the location of the potential buyer, without a commitment of shipping to the location of the potential buyer. A position of one or more of the may ship listings in the first ranking may be altered to achieve a second ranking.

Abstract: A system to generate an index for a closest match search is described. The system receives a corpus of information that includes member information. The system parses the member information to generate signatures for each of the members and stores the signatures in the index. The signatures are unique to the members. Accordingly, the signatures signify the respective members. The system subsequently utilizes the index to identify input information that matches signatures in the index to identify a closest match of the input information to one or more members in the corpus of information.

Abstract: A system to generate an index for a closest match search is described. The system receives a corpus of information that includes member information. The system parses the member information to generate signatures for each of the members and stores the signatures in the index. The signatures are unique to the members. Accordingly, the signatures signify the respective members. The system subsequently utilizes the index to identify input information that matches signatures in the index to identify a closest match of the input information to one or more members in the corpus information.