An email payment gateway having electronic commerce (e-commerce) system may send advertisement emails to customers that are registered with the e-commerce system. The advertisement emails may include mailto hyperlinks. Each mailto hyperlink may be associated with a product that is being offered for sale, and each mailto hyperlink describes an email message that may be generated when that hyperlink is selected. When a mailto hyperlink is selected, the generated email message may include one or more parameters related to the product associated with the hyperlink, such as an identifier of the product. The generated email message may then be transmitted to the e-commerce system. The e-commerce system may receive the message and, based on the parameters in the received message, execute a transaction to purchase the identified product on behalf of the customer.

There are provided systems and methods for automatic synchronization of a device for transaction processing based on geo-fenced locations. A merchant may geo-fence an area corresponding to the merchant's location where a user may visit to purchase an item. The merchant may provide multiple transaction processing and payment options at the merchant location, including payment applications with a mobile communication device. A payment provider may detect the location of the user and determine that the user's location matches the merchant's location through the geo-fenced area for the merchant. The payment provider may then configure a payment process for the user to utilize at the merchant location using payment mechanisms accepted at the merchant location. Where multiple merchants are located nearby, the payment provider may utilize additional known information for the user to select the most likely merchant for the user.

Systems and methods including one or more processors and one or more non-transitory storage devices storing computing instructions configured to run on the one or more processors and perform recording one or more actions of a user during an online browsing session; predicting, in real-time, a first user attribute for the user from one or more user attributes during the online browsing session based on the one or more actions of the user during the online browsing session; correlating the first user attribute, as predicted, for the user, wherein a first user preference of one or more user preferences is associated with the first user attribute, as predicted; and mapping each respective user preference of the one or more user preferences to a vector space, wherein: two or more user preferences of the one or more user preferences determined to be more similar to one another are closer together on the vector space than two or more user preferences of the one or more user preferences determined to be less similar to one another. Other embodiments are disclosed herein.

Supplemental content can be determined through a server-side process, wherein a primary content provider can obtain the supplemental content from a supplemental content provider before any of the content is sent to the client device initially requesting the primary content. A primary content provider submits a request that can include an authorization token for the primary content provider, a publisher token including a proxy identifier for the user, application, or client device, and other relevant information. The supplemental content provider can verify the information and select targeted content based on the information. The supplemental content can then be provided to the primary content provider for incorporation with the primary content. The primary content provider can adjust the layout or selection before sending to the client device for display.

An electronic marketplace provides a communication platform between consumer and sellers. Consumers create product wish lists and the wish lists are used as the basis for product advertising from the sellers. In addition, a consumer may invite friends to view the wish list and purchase particular products that are added to the wish list. Because wish lists are used as the basis for advertising, consumers can manage the types of product advertising that they see. In addition, consumers have the ability to turn off individual product advertisements by removing that product from the wish list. Sellers utilize reverse bidding to compete for advertising space with each consumer and friend. One particularly advantageous feature of the embodiment is that wish lists serve as nameless and untraceable proxies for each member by keeping personal information removed from the sellers.

This application relates to apparatus and methods for automatically determining and providing, for a given anchor item, a ranking of items. The ranking may include sponsored items. In some examples, a computing device receives a request for items for an anchor item. The computing device determines a relevancy of a plurality of recommended items and sponsored items. The computing device also determines a cost value for the sponsored items. The computing device determines ranking values for the plurality of recommended items and sponsored items based on the relevancy values and the cost values, and ranks the items based on the ranking values. In some examples, the computing device updates the final item ranking based on the application of one or more rules. The computing device transmits the final item ranking to a web server. The web server displays advertisements for the items in ranked order.

Among other things, a server controls access to private information about attributes of a unique item, during one or more stages of an online transaction for the unique item between a first online party and a second online party. The controlling of the access to the private information includes the server digitally storing (a) the private information, (b) a listing of the unique item for the online transaction received from the first online party, the listing including one or more constraints on access to the private information by the second online party at one or more stages of the online transaction, and (c) an indicator of a current stage of the one or more stages of the online sale transaction. The server allows or prevents access to the private information at one of the stages of the online transaction through a browser or an app installed on a device of the second online party and based on the current stage of the online transaction.

This application relates to apparatus and methods for providing recommended items to advertise. In some examples, a computing device determines a first set of items for recommendation based on historical user data associated with a user, and a second set of items for recommendation based on real-time user session data for the user. The computing device may then determine a subset of the first set of items based on associated scores and a predetermined threshold number of first items that can be presented for optimal user interaction. The computing device may generate a set of item recommendations by combining the subset of the first set of items and at least one of the second set of items to present to the user as advertisements.

Systems and methods including one or more processors and one or more non-transitory storage devices storing computing instructions configured to run on the one or more processors and perform acts of: providing a machine learning architecture that is configured to evaluate expensiveness of items relative to each other, wherein the items are included in an item type category; receiving prices associated with the items included in the item type category; generating, using a price band determination model associated with the machine learning architecture, price bands based, at least in part, on the prices associated with the items, each of the price bands being associated with separate price range boundaries for the item type category; and assigning each of the items to one of the price bands. Other embodiments are disclosed herein.

Systems and methods including one or more processors and one or more non-transitory storage devices storing computing instructions configured to run on the one or more processors and perform acts of: determining price bands for an item type category; associating items included in the item type category with the price bands; analyzing, using an affinity prediction model of the machine learning architecture, price band activity data indicating interactions of a user with respective items included in each of the price bands for the item type category; and generating one or more price affinity predictions for the user based, at least in part, on the price band activity data, wherein the one or more price affinity predictions predict a preference of the user for respective items associated with one or more of the price bands. Other embodiments are disclosed herein.