Systems and methods for determining how topics evolve in a messaging system extract at least one N-gram from data content (e.g., caption of messages) in the messaging system and detect anomalous behavior in N-gram frequencies over time. The anomalous behavior is used to select candidate N-grams for a determination of whether a topic of a candidate N-gram is evolving or fading. The candidate N-grams are clustered into cluster groups that are used to train at least one time series forecasting model to predict N-gram frequencies in a future time window. A time series of the N-gram frequency is divided into old and recent partitions and pattern recognition is applied to the predicted N-gram frequencies to identify an evolving or fading topic when the difference between a frequency of each anomaly and an average rolling median for each partition is greater for the most recent partition.

Provided are a payment method and system through generation of a one-time payment-only number of a real card linked with an application, wherein in order to make payment safely by generating a one-time payment-only number every payment without leaving card information of a user in a provider's payment terminal or by using the generated one-time payment-only number, when a real card is linked with an application installed on a user terminal and the payment terminal recognizes the real card, a user signature input window, and transaction information about payment performed with a one-time payment-only number generated by a card company server are displayed on a screen of the application of the user terminal, and payment is performed when a signature of the user is input to the signature input window.

In one example, a method performed by a processing system including at least one processor includes rendering an extended reality environment, monitoring social interactions of a plurality of users within the extended reality environment, adjusting the extended reality environment in response to the social interactions of the plurality of users, and adjusting a rule associated with the extended reality environment in response to the adjusting the extended reality environment.

A smart card, including a card with EMV contacts, for determining a person's preferences and for providing services.

An online system, such as a social networking system, generates shared models for one or more clusters of categories. A shared model for a cluster is common to the categories assigned to the cluster. In this manner, the shared models are specific to the group of categories (e.g., selected content providers) in each cluster while requiring a reasonable computational complexity for the online system. The categories are clustered based on the performance of a model specific to a category on data for other categories.

An online system generates a cluster group and uses membership in the cluster group as an eligibility criteria for presenting a content item. The online system receives a request from a third party system to present the content item. The online system also receives identification information about users who have visited webpages associated with the third party system and descriptive information associated with the webpages. Based on the descriptive information, the online system extracts tags for the webpages and classifies the webpages into one or more categories that include a category associated with the content item. The online system generates a seed group that includes users who visited webpages in the category associated with the content item. The online system further expands the seed group to a cluster group by applying a cluster model to one or more characteristics of each candidate user not included in the seed group.

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.

An automated advertising scheduling and distribution process reacts to the effectiveness of sales data. A hosted platform creates location-specific playlists based on key consumer variables that impact buying behavior, and dynamically performs data analytics. Utilizing a programmatic system and machine learning algorithmic methodology, the platform gathers data from the retailer's data warehouse and automatically pulls location-by-location sales data while simultaneously collecting playback data. If sales are not being affected on the particular item that is being promoted, then the platform may be configured to replace that message with a promotional message for another product with a higher likelihood of engagement and conversion. This virtual feedback loop ensures that the platform is optimizing the most effective series of promotional messages for any given location. The content management administrator accordingly delivers relevant advertising/messages to various display screens integrated into fuel pumps, through the store, and to retailer loyalty program applications.

Systems and methods for providing product samples to users in autonomous vehicles, thereby allowing businesses to advertise their products via the rideshare platform. A physical advertising platform is provided leveraging existing rideshare and delivery infrastructure to allow businesses to advertise their products by providing samples in rideshare vehicles and/or with rideshare vehicle deliveries. Consumers have the opportunity to try new products from local businesses that they may otherwise not be exposed to. In some implementations, a rideshare application interface can allow users to see what new local product samples are available. In some examples, a user can select one or more product samples to try.

An online system receives a content item including a link to a landing page and determines a likelihood the landing page violates an online system policy based on a structural similarity between the landing page and a web page violating the policy. To determine the likelihood, the online system determines a hierarchical structure associated with the web page violating the policy and an additional hierarchical structure associated with the landing page. The hierarchical structure represents a structure of at least a portion of the web page and the additional hierarchical structure represents a structure of a corresponding portion of the landing page. The online system compares the hierarchical structure and additional hierarchical structure. Based on the comparison, the online system computes a measure of dissimilarity between the hierarchical structure and additional hierarchical structure and determines a likelihood the landing page violates the policy based on the measure of dissimilarity.