A computer system, including: a downloadable application to operate on a mobile device of a customer, the downloadable application including instructions to browse and select items on web pages of an online seller, wherein the downloadable application enables the customer to select a desired price for a promotional product of the online seller; a consumer service provider in communication with the downloadable application of the mobile device, the consumer service provider to promote the promotional product of the online seller.

A computer-implemented system and method includes determining, by a mobile device associated with a user, a location of the user, generating, by the mobile device, a code comprising a tokenized value for sending funds and the location of the user, transmitting, by the mobile device, the code to a point of sale (POS) terminal associated with a merchant as part of a mobile wallet transaction, and receiving, by the mobile device, an indication that the mobile wallet transaction has been completed.

Topics of potential interest to a user, useful for purposes such as targeted advertising and product recommendations, can be extracted from voice content produced by a user. A computing device can capture voice content, such as when a user speaks into or near the device. One or more sniffer algorithms or processes can attempt to identify trigger words in the voice content, which can indicate a level of interest of the user. For each identified potential trigger word, the device can capture adjacent audio that can be analyzed, on the device or remotely, to attempt to determine one or more keywords associated with that trigger word. The identified keywords can be stored and/or transmitted to an appropriate location accessible to entities such as advertisers or content providers who can use the keywords to attempt to select or customize content that is likely relevant to the user.

Disclosed are various embodiments for establishing a connection between a client device and a third-party entity device and providing services associated with a third-party entity to the client device according to user-defined access permissions. A context environment can be determined according to user data and third-party entity data. Services available to the user device can be selected according to the context environment, the user-defined access permissions and third-party defined instructions. Upon selecting the services, the services are provided to the client device and a connection between the client device and a third-party entity device can be established.

Example prediction methods and apparatus are described. One example includes sending a first model parameter and a second model parameter by a server to a plurality of terminals. The first model parameter and the second model parameter are adapted to a prediction model of the terminal. The server receives a first prediction loss sent by at least one of the plurality of terminals. A first prediction loss sent by each of the at least one terminal is calculated by the terminal based on the prediction model that uses the first model parameter and the second model parameter. The server updates the first model parameter based on the first prediction loss sent by the at least one terminal to obtain an updated first model parameter. The server updates the second model parameter based on the first prediction loss sent by the at least one terminal to obtain an updated second model parameter.

Disclosed is a method and system for providing a behavior data sales service. A behavior data sales service providing method, implemented by a computer system, may include collecting behavior data of each of activity entities; associating the behavior data with unique information of each of the activity entities; generating sales data of each of the activity entities by assigning a valid period for the unique information of each of the activity entities to the unique information of each of the activity entities; monitoring the valid period for the unique information of each of the activity entities in response to selling sales data of each of the activity entities to each of purchase entities; and repeatedly updating the unique information of each of the activity entities according to the valid period for the unique information of each of the activity entities.

The present disclosure relates to improving recommendations for small shops on an ecommerce platform while maintaining accuracy for larger shops. The improvement is achieved by retraining a machine-learning recommendation model to reduce sample selection bias using a meta-learning process. The retraining process comprises identifying a sample subset of shops on the ecommerce platform, and then creating shop-specific versions of the recommendation model for each of the shops in the subset. Each shop-specific model is created by optimizing the baseline model to predict user-item interactions in a first training dataset for the applicable shop. Each of the shop-specific models is then tested using a second training dataset for the shop. A loss is calculated for each shop-specific model based on the model's predicted user-item interactions and the actual user-item interactions in the second training dataset for the shop. A global loss is calculated based on each of the shop-specific losses, and the baseline model is updated to minimize the global loss.

One or more computing devices, systems, and/or methods are provided. In an example, purchase data associated with users may be determined. The purchase data may be indicative of purchases by users from entities. The purchase data may be analyzed to determine purchase metrics associated with the users. The purchase metrics may be analyzed to determine sets of groups of users associated with the entities. One or more groups of users, of the sets of groups of users, that include the user may be determined. Content may be selected for presentation via a first device associated with the first user based upon the one or more groups of users.

A press release distribution system provides press release and other news to forum sites as posts. The forum software that runs at forum sites includes press release interface software or is adapted to receive press release interface plug-in modules for interfacing with the press release distribution system. The press release interface software or plug-in module may also monitor and/or analyze user data of forum members and/or forum activities of the users. The monitored user data and forum activities may be provided to the press release distribution system for analysis and generation of user profiles. Using the result of the analysis (e.g., user profiles), the press release distribution system can target particular users or forums to direct the press releases, news, or advertisements for most effective advertising campaign.

A messaging system provides recommendations of content that account holders of the messaging system might be interested in engaging with. In order to determine what to recommend, the messaging system generates a model of account holder engagement behavior organized by type of engagement. The model parameters are trained on differences between expected engagement behavior based on past data and actual engagement behavior, and include a set of common factor matrices that are trained using data from more than on engagement type. As a consequence, engagement behavior of other account holders with respect to other types of engagements different than the one sought to be recommended serves as a partial basis for determining what engagements of the sought-after type are recommended.