Methods, systems, and apparatus, including computer programs encoded on a computer-readable storage medium, for providing content. A method includes receiving a request for an advertisement to be displayed in a slot associated with a third-party content site; identifying a relevant advertisement to be provided in the slot; determining information to be included in an annotation associated with the advertisement, the annotation including customized information to be presented along with the advertisement; providing the advertisement responsive to the request including providing the annotation along with a control for re-publishing the advertisement along with the relevant advertisement; receiving user input selecting the control and designating the advertisement for re-publishing to a group, the group being designated by the user; and targeting additional content to the group based on the received user input.

Examples disclosed herein relate to performing an action based on an advertisement indicator is present in a network packet. In one example, a network packet identified by a network infrastructure device as matching criteria associated with a pre-identified request is received. A parameter within the network packet is identified. A rule is used to determine whether the parameter indicates that an advertisement indicator is present in a flow associated with the network packet. An action is performed based on whether the advertisement indicator is present in the network packet.

The present teaching relates to generating an updated model related to advertisement selection. In one example, a request is obtained for updating a model to be utilized for selecting an advertisement. A plurality of copies of the model is generated. The model is pre-selected based on a performance metric related to advertisement selection. Based on each of the plurality of copies, a candidate model is created by modifying one or more parameters of the copy of the model to create a plurality of candidate models. One of the plurality of candidate models is selected based on the performance metric. The steps of generating, creating, and selecting are repeated until a predetermined condition is met. The model is updated with the latest selected candidate model when the predetermined condition is met.

Provided are various mechanisms and processes for generating dynamic merchant scoring predictions. A system is configured to receive datasets comprising pairings between training customer profiles and training merchant profiles. For each pairing, a set of feature values corresponding to features specified by the customer and merchant profiles are extracted and converted into a training vector to train a machine learning model to determine a weighted coefficient for each feature. Once sufficiently trained, the system determines a set of available merchant profiles for a customer profile in response to receiving a search request from a customer associated with the customer profile. For each pairing between the customer profile and an available merchant profile, the system determines an order score for the available merchant based on the weighted coefficients and an input set of feature values specified by the customer profile and the available merchant profile.

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.

Computer-implemented methods of providing personalized recommendations to a user of items available in an online system, and related systems. First-level features including context features are computed based upon context data. A first-level machine learning model is then evaluated using the first-level features to generate predictions of user behavior in relation to a plurality of individual items available via the online system. A list of proposed item recommendations is constructed based upon the predictions. Second-level features are computed based upon the context data and list features based upon the list of proposed item recommendations and the corresponding predictions generated by the first-level machine learning model. A second-level machine learning model is evaluated using the second-level features to generate a prediction of user behavior in relation to the list of proposed item recommendations. A personalized list of item recommendations is provided based upon the prediction generated by the second-level machine learning model.

This document describes the autonomous collection, generation, distribution, and management of online web content. The devices, systems, and methods described herein can be used to collect and generate online web content and communications in an automatic and autonomous manner. Specifically, the disclosed methods, devices, and systems may be employed to produce one or more communications and/or advertising campaigns, as well as for monitoring, managing, defining the efficiency, effectiveness, and workability of the campaign with respect to generating predicted user engagements, thereby accurately determining the cost benefits of the communication campaign. The system may track, evaluate, and provide analytic results that may then be used to better guide the system parameters for customizing autonomous communications directed one or more characteristics of a defined target audience.

The present disclosure provides a computer-implemented method and system for granular level segmentation of users based on online activities on a webpage in real-time. The computer-implemented method and system corresponds to a user segmentation system. The user segmentation system receives a first set of data associated with a plurality of users. The user segmentation system fetches a second set of data. The user segmentation system obtains a third set of data. The user segmentation system analyzes the first set of data, the second set of data and the third set of data using one or more machine learning algorithms. The user segmentation system creates one or more segments based on analysis performed on the first set of data, the second set of data and the third set of data. The user segmentation system initiates one or more marketing campaigns for the one or more segments.

A server and a method for real-time targeting of advertisements across multiple platforms is provided. The server receives first information that indicates a presence of a first person in a first vehicle at a first time period. The server determines content metadata associated with media content rendered via an infotainment device of the first vehicle. The server transmits first advertisement content to the infotainment device. The server receives second information that indicates an absence of the first person from the first vehicle at a second time period. The server further determines a first application used by the first person on an electronic device within a time threshold from the second time period. The server further transmits second advertisement content, to be rendered on the determined first application, to the first electronic device. The second advertisement content may be associated with the first advertisement content.

An acquirer acquires last date information and at least one of frequency information and money amount information. The last date information indicates a last store visit date when a customer last visited a store or a last purchase date when the customer last purchased a product at the store. The frequency information indicates at least one of a frequency with which the customer has visited the store and a frequency with which the customer has made a purchase at the store. The money amount information is information about an amount of money of products purchased by the customer at the store. A product selector selects a product whose advertisement is required to be presented to the customer using at least one of the frequency information and the money amount information. On the basis of the last date information, an information amount determiner sets a condition.