A communication system includes a transmitting device configured to transmit information, and a receiving device configured to receive the information, the receiving device includes a determination unit configured to determine whether or not an electronic certificate of the transmitting device used for a communication with the transmitting device is an EV certificate, and a process that is performed is varied according to a determination result of the determination unit.

An online system accesses a model trained based on a topic associated with a set of content items and the content of the set of content items. The online system applies the model to predict a probability that each of multiple content items is associated with the topic based on its content and identifies (a) content item(s) associated with at least a threshold probability. The online system retrieves information describing user engagement with the identified content item(s) and determines a value of a performance metric for the topic based on this information. If the value is at least a threshold value and the online system receives content from an entity describing an item associated with the topic, the online system communicates a recommendation to the entity to create a content item describing the item and to add a tag associated with the item upon determining an opportunity to do so.

There is disclosed a system and a method for training an MLA to predict a visibility score indicative of a likelihood of a targeted message included within a web resource, the method being executable on a server, the method comprising: generating a training dataset by retrieving a plurality of training targeted messages, the training web resource having a plurality of targeted message slots for placing therein one or more training targeted messages; causing, the training electronic device to display the training web resource; upon the training user accessing the training web resource using the training electronic device during a subsequent instance of time, causing the training electronic device to display the web resource; tracking an activity parameter, the activity parameter being indicative of an interaction by the training user with the given one of the plurality of training targeted messages; generating the training dataset.

The demand-side platform (DSP) is a technological ingredient that fits into the larger real-time-bidding (RTB) ecosystem. DSPs enable advertisers to purchase ad impressions from a wide range of ad slots, generally via a second-price auction mechanism. In this aspect, predicting the auction winning price notably enhances the decision for placing the right bid value to win the auction and helps with the advertiser's campaign planning and traffic reallocation between campaigns. This is a difficult task because the observed winning price distribution is biased due to censorship; the DSP only observes the win price in the case of winning the auction. For losing bids, the win price remains censored. In this invention, we generalize the winning price model to incorporate a gradient boosting framework adapted to learn from both observed and censored data. This yields a boost in predictive performance in comparison to classic linear censored regression.

Disclosed herein is a web user experience improvement for digital magazines. A digital magazine viewing platform is integrated with a digital magazine publishing platform including features that leverage the integration including user interface arrangement based on viewing habits and ripped content that is insertable into draft digital magazine documents. In some embodiments, a machine learning model categorizes magazine styles and present publishing features based on those magazines viewed or subscribed to by a given user.

Targeting content to users based on receipt of partial terms may include identifying one or more terms associated with a campaign, the campaign having an associated content item that is presented to users responsive to requests for content, presenting a campaign sponsor with an option to target the content item to users based on receipt of a partial form of one of the one or more terms, receiving from the campaign sponsor a selection of a designation of the partial form of the term for use in targeting, and optionally presenting the content item in a search suggestion control along with search completions in response to receipt of the partial form of the term in a search control.

Systems and methods are provided for receiving a first message associated with a first sponsor and a second message associated with a second sponsor for inclusion in a message collection. The systems and methods determine a first priority parameter associated with the first message based on sponsored content, and a second priority parameter associated with the second message based on sponsored content. Based on a determination that there is insufficient message inventory to include both the first message and the second message in the message collection, the systems and methods prioritize the first message associated with the first sponsor in the message collection and excluding the second message associated with the second sponsor in the message collection, based on an amount of consideration associated with the first priority parameter received from the first sponsor and an amount of consideration associated with the second priority parameter received from the second sponsor.

This application includes a media resource allocation method, performed by any media resource allocation server in a blockchain system. In the method, media resource information is received from a media resource server. Media resources for a plurality of media resource request servers are allocated according to (i) media resource requirements of the plurality of media resource request servers, (ii) the media resource information, and (iii) a preset allocation rule. The transaction data generated in the media resource allocation process is stored into the target blockchain.

Sponsored and organic pieces of content are displayed in accordance with a blending model that is used to first identify a pattern of slots to which to assign either sponsored or organic pieces of content. This blending model is applied to a combination of both sponsored and non-sponsored pieces of content being considered for display to a user. This pattern only determines the slot assignments. The actual ranking of the pieces of content, and more particularly the actual ranking of the organic pieces of content, is determined by an ordering other than the ranking determined by the blending model, such as by using the original ordering of the second list. The pieces of content are then displayed in the order of this actual ranking, but in the slots indicated as having been assigned to be either sponsored or organic in the pattern determined by the blending model.

Shaded bid values may be determined and/or submitted to one or more auction modules for participation in auctions. Auction information including at least one of impression indications associated with the auctions, sets of features associated with the auctions, the shaded bid values associated with the auctions, etc. may be stored in a database. A machine learning model may be trained using the auction information to generate a first machine learning model with feature parameters associated with features. A bid request, indicative of a second set of features, may be received. The first machine learning model may be used to determine win probabilities and/or expected bid surpluses associated with multiple shaded bid values based upon one or more feature parameters, of the feature parameters, associated with the second set of features. A shaded bid value for submission may be determined based upon the win probabilities and/or the expected bid surpluses.