The present disclosure is directed to methods for identifying a user by a demand side platform (DSP) across advertiser exchanges. The method includes establishing, by a DSP, a cookie mapping for a user. The cookie mapping includes a mapping of user identifiers for the user from advertisement exchanges to a user identifier assigned by the DSP for the user. The DSP stores to the cookie mapping a first mapping to the user identifier of the DSP, comprising a first user id received by a bidder from a first exchange and a first exchange id for the first exchange. A bidder inserts a pixel into a bid for an impression opportunity to a second exchange. The pixel includes a key to the cookie mapping and a second user id for the user and a second exchange id. The second user id is received by the bidder from a second exchange.

The present disclosure discloses a method and device for pushing information to a target user. One example method includes identifying a plurality of users that meet a predetermined condition; selecting a target user from the identified users to be provided with information, where in the target user is selected based on a relationship strength and an influence of the identified users; and transmitting the information to the determined target user over a network, and relates to the field of information technologies.

Techniques are disclosed to determine when a user is in need of a loan and notifying the user of loan offers. With user permission or affirmative consent, user data may be monitored for several users, which is used to build a user profile for each user. The user profile may then be analyzed to determine whether a user will require a loan within a future time period. To do so, the user data may include data from various sources, which indicate the user's interactions and behaviors such as demographic data, data indicative of user shopping habits, online browsing, life events, or other relevant behaviors. This data may then be analyzed to predict a statistical likelihood that a user will need a loan. When this statistical likelihood is exceeded, a user may be preapproved for a loan and/or a targeted notification may be sent indicating offers for certain types of loans.

A system for capturing impression data includes a server in communication with a user's computing device via a communications network. The server is configured to generate a pixel for embedding in a digital communication viewable in a web browser on the user's computing device. The pixel is served from a domain associated with the server. The server is configured to, in response to the digital communication being viewed in the web browser on the user's computing device, set a cookie on the user's computing device via the pixel. The cookie is configured to store data associated with one or more impressions of digital communications viewed on the user's computing device. The server is configured to, in response to the user's computing device accessing the domain via the web browser, receive the stored data associated with the one or more impressions from the cookie set on the user's computing device.

A method for identifying a simulated social media account history is provided. The method may include querying a social media account to obtain social media identification information. The querying may determine whether the account history includes one or more parameters that indicate whether the social media account is related to an automated entity or a human entity. The parameters may include at least one of less than a threshold number of friends on the account; more than a threshold frequency of historic ticket purchases per unit time; disparate location of historic ticket purchases per unit time and/or a historic record of less than a threshold reaction time to a plurality of ticket offers.

Methods and systems are provided that may be utilized to detect occurrence of one or more specified circumstances. A determination may be made as to whether one or more specified circumstances are detected such as responsive to one or more user actions or an occurrence of an event unrelated to a user. One or more binary digital signals may be generated to store a detection of one or more specified user circumstances in a log or memory at least partially in response to detection of the one or more specified circumstances.

A computer-implemented method can facilitate delivery of targeted content to user devices in situations in which historic tracking data (e.g., cookie data) is generally unavailable and/or unreliable. A p-dimensional embedding of websites can be generated based on a group of user devices for whom tracking data is available. Conversion event data that indicates indicating whether that audience member performed a conversion action can be received. A machine learning model can be trained using the conversion event data and the positions of websites appearing in the conversion event data within the p-dimensional embedding to predict a likelihood of conversion and/or a type of content to provide given a position in the p-dimensional embedding. When an indication that a user device is accessing a website is received, a position of that website in the p-dimensional embedding can be determined and targeted content can be delivered to the user device.

A method of delivering advertising in an online environment includes determining a context of a user operating a client computer to interact with an e-commerce website, where the determined context representing an intent of the user to locate a product for purchase, defining a relation between one or more of a plurality of advertisements and the product based on at least one of a plurality of relevance types, and displaying, to the user, at least one of the advertisements having the relation to the product.

An online social network is provided. A sentiment is determined for each of a plurality of users of an online social network (OSN) in relation to a first product. A category is determined for each of the plurality of users based, at least in part, on the sentiment of each of the plurality of users, respectively. A group including a first user and a second user of the plurality of users is generated based, at least in part, on the category of each of the first user and the second user and a relationship within the OSN between the first user and the second user. An advertisement is presented to the first user. An indication is presented to the first user that the advertisement is also presented to the second user.

A system can perform certain acts. The acts can include determining score values to identify a plurality of target customers associated with a plurality of potential offers. The acts can include receiving bids from the plurality of target customers for the plurality of potential offers. The acts can include performing an iterative process for each respective target customer of the plurality of target customers to take turns to submit a respective bid for each respective potential offer of the plurality of potential offers associated with the respective target customer. The acts can include determining a respective final bid for each of the plurality of potential offers such that an aggregate value for the plurality of target customers that can be maximized across the plurality of potential offers. The acts can include sending instructions to deliver the plurality of offers to at least a portion of the plurality of target customers.