This application relates to systems, methods, devices, and other techniques that can be utilized to simulate and analyze behaviors of customers in a retail environment.

A system, method, device, and platform for performing a gaming utilizing blockchain. A player profile is created in response to information received from a player. Gaming information is received from the player associated with the one or more games. Selections are assigned to the player for the one or more games utilizing player profile and the gaming information. Winners and a host with each of the one or more games of the one or more games are compensated once the one or more games are completed. The player profile, the gaming information, the selections, and the winners are documented utilizing the blockchain.

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 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.

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.

Method, system, and media for improving a fan experience for an event at a venue with a live audience. Broadly speaking, embodiments of the invention provide techniques for systematizing fan interactions based on automatically detected fan groupings and criteria triggering activation events. Fans are classified into audience groupings based on collected data associated with each fan. Fan interactions map activation criteria (such as activities performed by the fan or purchases made by the fan, information about the event, or the ambient conditions) to activation events that can collect additional data about the fan, incentivize the fan to make a purchase, or improve the fan experience in some other way.

Systems and methods are disclosed for providing non-intrusive advertising content to users. The method includes receiving user input reflecting user content preferences for receiving desired content related to specific topics in lieu of standard advertising slots while watching playable media content via a set-top box. The method may further include receiving a request for playable media content and then retrieving desired content based on user content preferences. Thereafter, the method may include choosing non-intrusive advertising content and generating datagrams that include the desired content and the non-intrusive advertising content. The datagrams may be concatenated with the playable media content to generate displayable content. The display content may then be provided for displaying.

Methods and systems for facilitating generation of electronic advertising messages based on model web pages. A visual content request is received that identifies a model web page. Source code is retrieved that includes patterns that can be matched to identify visual features of the model web page. Visual content information is provided to an advertising message generation service to construct advertising messages that include at least a portion of the identified visual features. Constructed advertising messages are received, including at least one modified advertising message that incorporates a user-selected image extracted from the model web page. The identified visual features of the model web page can include text format, text color, background color, images, and video sequences.

A product recommendation method includes an indexing step implementing a Bayesian network capable of creating direct matches between each respective product of a product catalog and an ideal user whose characteristics are the most likely to be suitable for the respective products. The indexing step includes inputting a first descriptor vector of a product into the Bayesian network to obtain the user-characteristics most likely to be suitable for the product. The product recommendation method further includes a refining process which identifies determinants associated with nodes and conditional probabilities of the Bayesian network to minimize errors at the nodes to which the arcs of the Bayesian network point.