Systems and methods are disclosed for protecting user privacy in, for example, online advertising environments. The method includes receiving data related to a user in a first communication session between a host server and a client device, and generating a user profile associated with the user. The method further may include encrypting the user profile to produce encrypted user profile data and generating a decryption key for decrypting the encrypted user profile data. Thereafter, either the decryption key or a portion of the encrypted user profile data may be transmitted to the client device and then deleted from host server before ending the first communication session. The method further may include establishing a second communication session between the host server and the client device and retrieving the transmitted content. Then targeted advertising may be provided by decrypting the encrypted user profile data.

A system configured to determine the value of channels responsive to users requesting installation of a client application through the channels. The client application may provide access to an online game. A given channel may be characterized by a promotional information item, a platform through which the promotional information item is presented to potential users, and/or other information. Predicted economic values for the users that requested installation of the client application through one or more channels may be determined based parameter values for the users. Effectiveness metrics for the channels may be determined based on the predicted economic values of the users. Subsequent consideration expenditures for one or more of the channels may be recommended and/or executed based on a comparison effectiveness metrics determined.

An iterative method for optimizing targeted advertisement distribution for a social network including a plurality of users, the method including the steps of creating a user summary for a user by extracting persona attributes of a user account, generating a promotion summary for each of a plurality of advertisements, selecting an advertisement for the user based on the similarity between the promotion summary of the advertisement and the user summary, assessing a user reaction to the advertisement, and updating the user summary and promotion summary based on the user reaction.

In response to detecting that a first selectable element on a webpage or application page of a merchant has been accessed by a user, a computer system stores a visual representation of an item that corresponds to the first selectable element in a first space of the user. In response to detecting a selection of a second selectable element from within the first space, the computer system determines a threshold number of users to associate with the visual representation and creates a post in a network feed that includes the visual representation, wherein a third selectable element is associated with the post, that when selected, causes a user to join or vote for the first visual representation. In response to determining that a number of users that have selected the third selectable element meets the threshold number of users, the computer system initiates a group purchase for the item.

The invention provides, in some aspects, a statistical algorithm-driven digital system for automated optimization of a large number of key performance indicators (KPI) involved in social digital interactions among the users, contents and advertisement, further augmented by data-driven verification and recommendation. The users include humans from diverse socio-cultural-economic groups, whose identity may be pseudonymous (though persistent), and whose explicit features may remain private, though statistically imputable. The contents include webpages, downloads, videos, music, or other content accessed by the users. The advertisements include product placement, branding, appeal, surveys, or other third-party contents, not explicit sought by the user. A server application executing on the server digital device responds to requests received from the client digital devices for delivering thereto requested digital content. The server application customizes at least a selected piece of digital content it delivers to a respective client application (in response to such a request) based on ordinal rankings for users, contents and advertisements, computed by a tensor based statistical inference algorithm, as described in a preferred embodiment of this invention. The rankings computed are predictive of various aspects of user's future social interactions, as determined by the past statistical data, summarized in sparse high-dimensional tensors.

A determination device according to the present application includes an acquisition unit, an estimation unit, and a determination unit. The acquisition unit acquires user information as information about a user. The estimation unit estimates a degree of contribution of the user to a predetermined service based on the user information acquired by the acquisition unit. The determination unit determines an aspect of providing information content to the user based on the degree of contribution estimated by the estimation unit. Accordingly, the determination device according to the present application improves cost effectiveness of the information content.

A method for tracking a user across multiple website domains over a computerized network, the method comprising sending a first first-party user identifier to a collector in response to a user accessing a first website; generating a first user prompt in response to the user accessing the first website; receiving a first input from the user in response to the first user prompt; redirecting the user to a centralized domain; sending a second first-party user identifier associated with the user and the centralized domain to the collector; correlating the first first-party user identifier with the second first-party user identifier to determine that the user is associated with both the first first-party user identifier and the second first-party user identifier; and displaying a webpage of the centralized domain or a landing page in response to the correlation.

Methods and apparatus to collect and process browsing history are disclosed. One disclosed method of collecting browsing history includes collecting a plurality of web requests, and for a web request in the plurality of web requests, determining a count indicating a number of other ones of the plurality of web requests that include a referrer identifying the web request. The method also includes when the count meets a threshold, indicating that the web request is a parent web request.

A method and a system for predicting and using customer lifetime value (CLV). The method include: providing a classifier trained using customer feature data during a first period of time as input and whether there is spending during a second period of time as classifier label; providing a regressor trained using the customer feature data during the first period of time as input and amount of spending during a second period of time as regressor label; performing the classifier using customer feature data during a third period of time to obtain customers having positive predicted classifier labels; and performing the regressor using the customer feature data during the third period of time for the customers having positive predicted classifier labels, to obtain CLVs of the customers.

An online system identifies target audience for distributing content items based on historical activity of users. The online system receives information describing events representing actions of users performed on the online system or on an external system and stores the information as action logs. The online system receives targeting criteria specified as expressions, for example, expressions specifying aggregate values determined over a plurality of actions of a user. The online system retrieves events from the action logs and determines partial results based on expressions representing the targeting criteria. If the partial results for a user indicate that the user satisfies the targeting criteria, the online system selects the user for targeting the corresponding content item. The online system may process events in batches.