A method for training a model and an information recommendation system are provided. The method includes the following. Multiple types of features of the target model are obtained and a feature group sequence of the multiple types of features is generated, where the feature group sequence includes multiple feature groups and a sequence relation between the multiple feature groups and each feature group contains at least one type of features among the multiple types of features. The target model is classified into a multi-level model according to the feature group sequence. A trained target model is obtained by executing a preset training operation on the feature weight values corresponding to each level of the multi-level model.

An apparatus for providing customized advertisements includes a database that stores a plurality of electronic advertisements, receives biometric information of a client from at least one biometric device of the client, and receives receptivity information of the client responding to the plurality of electronic advertisements, as well as a processor that accesses the database, and maps the biometric information and the receptivity information and analyzes the mapped information to generate customized marketing data. The processor also calculates a receptivity probability for each of the plurality of electronic advertisements based on the customized marketing data by using current biometric state of the client, selects an electronic advertisement from the plurality of electronic advertisements based on the calculated receptivity probabilities, and outputs to the client the selected electronic advertisement.

Systems and methods are disclosed for online distribution of content based on a user sentiment index. The method may include receiving, over a network and from a user device, one or more user generated inputs and calculating the user sentiment index based on the one or more user generated inputs. The method may also include receiving, over the network, from a content or advertising provider, instructions on publishing content or advertising to a webpage based on the calculated user sentiment index, and publishing content for display on user devices over the network based on a comparison of the calculated user sentiment index and the received instructions.

An information providing method and apparatus a) transmits a request via a network, for recommended driving information for a first user to be received and displayed on a display of the first user, b) receives from the network driving data from a plurality of vehicles about how a plurality of users drive their vehicles including the first user, c) extracts a similar user from among the plurality of users who drives a vehicle with a predetermined similarity to how the first user drives a vehicle, and determines recommended driving information of the similar user from the similar user's driving history, d) transmits over the network to the first user the recommended driving information of the similar user, and e) displays on a display of the first user the recommended driving information of the similar user.

Provided is a process, including: obtaining a first training dataset, training a first machine-learning model on the first training dataset, obtaining a set of candidate question sequences, forming virtual subject-entity records, forming a second training dataset, training a second machine-learning model, and storing the adjusted parameters of the second machine-learning model in memory.

A system includes a first computing device client associated with a first user in a community of users operable to send content to publish to a data aggregation server. The data aggregation server is operable to receive the content to publish from the first computing device client, host a first user profile associated with the first user of the first computing device client, the first user profile identifying a targeted recipient in the community of users, and disseminate automatically the content received from the first computing device client to a second computing device client associated with the targeted recipient, without receiving input from the first computing device client explicitly specifying the targeted recipient to whom the content is disseminated.

Systems and related methods of providing seamless transactions, including payments and promotion redemptions, are discussed herein. Some embodiments may provide for a system including a communication beacon and one or more servers. The communication beacon may be configured to communicate with consumer devices via a direct wireless connection when the consumer devices are within a wireless direct communicable range of the communication beacon. The one or more servers may be configured to associate the communication with a merchant and receive consumer presence data from the communication beacon such that consumer presence at the merchant may be determined. In response, the one or more servers may be configured to perform a seamless transaction on behalf the consumer that allows the consumer to provide payments or redeem promotions, among other things, simply by self-identifying with the merchant while carrying the consumer device.

Systems and methods are presented for adding system-identified content to a user's content space based on information identified from the user's email account. In operation, after obtaining user authorization to access email items of an email account, the email items are iteratively processed. In processing each of the email items, one or more topics of a currently processed email item are identified. Based on the identified topics and user preferences, one or more content items may be identified for addition to the user's online content space. Additionally, the information identified from processing the one or more email items is used to update the user's preferences.

The current invention relates to a computer-generated method for optimizing placement of advertising content across multiple different devices. The system can allocate advertising campaigns and plans to various inventory types based on the probability of accurate consumer matching. Consumer matching can be achieved by generation of look-alike models in a consumer's device graph to predict future consumption behavior. The system includes an interface through which an advertiser can access relevant information about inventory and success of a given placement.

Systems and methods are described herein for inserting contextual advertisements into a virtual environment. While presenting a virtual environment, a media device receives speech input. The media device selects an advertisement from an advertisement database based on the speech. The media device then determines the current environment of the virtual environment and generates a virtual object that is related to the current environment. The media device overlays the selected advertisement over the generated virtual object and displays the generated virtual object in the virtual environment.