Abstract: One or more computing devices, systems, and/or methods are provided. 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 minimum bid values to win associated with the auctions, sets of features associated with the auctions, the shaded bid values associated with the auctions, unshaded bid values associated with the auctions, etc. may be stored in a database. A machine learning model may be trained using a loss function and/or 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 a shaded bid value for submission based upon one or more first feature parameters, of the feature parameters, associated with the second set of features.

Abstract: One or more computing devices, systems, and/or methods for generating dynamic content item recommendations are provided. Content item information, extracted from message data, is aggregated to calculate popularity and attributes of content items. The content items are ranked based upon the popularity and attributes to generate a ranked list of content items. Exploration traffic is served utilizing a set of eligible content items selected from the ranked list of content items. An eligible content item is promoted for participation in auctions for serving non-exploration traffic based upon the eligible content item being served a threshold number of times.

Abstract: Allocating bids for providing content within a segmented campaign is controlled to ensure that an event rate associated with the provided content meets or exceeds a threshold rate. A campaign-level event rate, associated with the provided content, is estimated and provided as a feedback signal. This feedback signal is employed to dynamically update bid allocations for each of the segments, which in turn varies the number or rate of provided impressions and events. Such feedback enables the control of the campaign-level rate and ensures that the campaign-level rate meets or exceeds the rate threshold. To control these rates, the number of total impressions, as well as the number of associated events, is temporally sampled across the campaign segments. Based on the number of impressions and events, the campaign-level event rate is estimated and employed as the feedback signal. Updating the bid allocations may be based on the Beta Distribution.

Abstract: One or more computing devices, systems, and/or methods are provided. A machine learning model may be trained using a plurality of sets of information. One or more pruning operations may be performed in association with the training to generate a machine learning model with sparse vector representations associated with features of the plurality of sets of information. A request for content associated with a client device may be received. A set of features associated with the request for content may be determined. A plurality of positive signal probabilities associated with a plurality of content items may be determined using the machine learning model based upon one or more sparse vector representations, of the machine learning model, associated with the set of features. A content item may be selected from the plurality of content items for presentation via the client device based upon the plurality of positive signal probabilities.

Abstract: The present teaching relates to a method and a system for advertising. The method obtains information about a conversion associated with an advertisement and generates with respect to the conversion, an operational smart attribution evaluation package (SAEP). The SAEP includes a conversion parameter and a reward. The method transmits the SAEP to a platform to be posted, and thereafter receives from the SAEP, an indication of an entity which is estimated to be associated with the conversion and to which the reward is to be allocated. The entity is determined by the SAEP based on the conversion parameter and information from a plurality of entities that displayed the advertisement.

Abstract: One or more computing devices, systems, and/or methods are provided. Activity of a plurality of devices may be analyzed to identify network traffic. A network profile associated with the plurality of devices may be generated based upon the network traffic. The network profile may be indicative of one or more sets of transmission metrics associated with a first device of the plurality of devices. The network profile may be indicative of one or more sets of reception metrics associated with the first device. Content may be generated based upon the network profile. The content may be transmitted to a device based upon the network profile.

Abstract: Disclosed are systems and methods for improving interactions with and between computers in content providing, searching and/or hosting systems supported by or configured with devices, servers and/or platforms. The disclosed systems and methods provide a novel framework for performing automatic detection of surfaces in video frames resulting in the creation of a seamless in-video ad experience for viewing users. The disclosed framework operates by leveraging available surfaces in videos to show advertisements in compliance with publisher protection, compliance and policy in a fully automatic, end-to-end solution. The disclosed framework evidences a streamlined, automatic and computationally efficient process(es) that modifies digital content at the surface level within the frames of the content in compliance with the digital rights of the owners of the content being merged via the disclosed augmentation.

Abstract: The present teaching relates to method and system for advertising. The method obtains information related to an operational smart attribution evaluation package (SAEP) posted on a platform. The SAEP is related to a conversion associated with an advertisement displayed by a plurality of entities. The method parses the SAEP to obtain a conversion parameter, and generates a claim for the conversion based on the conversion parameter and information related to the advertisement, if the advertisement was previously displayed. Further, the method transmits the generated claim to the SAEP to be evaluated whether a reward is to be allocated.

Abstract: Systems and methods are disclosed for executing the electronic distribution of electronic content to a dynamic display.

Abstract: Methods and apparatus for delivering content to an audience member via one or more mediums based on an audience member profile are disclosed. Profile data for audience members may be initially collected from an offline source, such as a registration or subscription database. The profile data may be stored in a dedicated database. The initial profile data may be supplemented periodically with data reflecting online activity by the audience member. The combined offline and online profile data may be used to group the audience members into segments. Audience member segments may be used to identify audience members who are targeted to receive like content. An audience member's inclusion in a segment may be indicated by storing a segment-targeting cookie on the audience member computer. Content may be delivered to the audience member based on identification of the segment in the segment-targeting cookie.

Abstract: Systems and methods are disclosed for providing non-intrusive advertising content to users. The method includes receiving user input reflecting user content preferences for receiving desired content related to specific topics in lieu of standard advertising slots while watching playable media content via a set-top box. The method may further include receiving a request for playable media content and then retrieving desired content based on user content preferences. Thereafter, the method may include choosing non-intrusive advertising content and generating datagrams that include the desired content and the non-intrusive advertising content. The datagrams may be concatenated with the playable media content to generate displayable content. The display content may then be provided for displaying.

Abstract: One or more computing devices, systems, and/or methods for generating and providing recommendations of products are provided. For example, content is extracted from a message sent to a user. The content is evaluated to identify a product identifier corresponding to a product title of a product. If the product identifier is a truncated version of the product title, then a database of product titles and frequencies of occurrence of the product titles is used to complete the product title. A model is used to infer a product category for the product title. Matching scores are assigned to products within a product category based upon weighted attributes. A recommendation is provided to the user for a product having a matching score greater than a matching threshold.

Abstract: In an example, sets of event information associated with events may be identified. The events may include intentional click events, accidental click events and/or skip events. Accidental click probabilities associated with the accidental click events and/or the skip events may be determined. Machine learning model training may be performed, using the sets of event information associated with the events and labels associated with the events, to generate a first machine learning model. The labels may include second labels associated with the intentional click events and/or third labels associated with the accidental click events and/or the skip events. The second labels may correspond to an intentional click classification. The third labels may be based upon the accidental click probabilities. Click probabilities associated with content items may be determined using the first machine learning model. A content item may be selected for presentation via a client device based upon the click probabilities.

Abstract: Systems and methods are disclosed for executing an online auction of diverse online advertisements. One method includes receiving inventory information for serving ads on a publisher web page, the inventory information including at least a first ad configuration different from a second ad configuration, each of the first and second ad configurations defining one or both of an ad size and an ad attribute; receiving or generating a plurality of ad bids to serve ads in the first ad configuration and to serve ads in the second ad configuration; and awarding impressions to one or both of the bids to serve ads in the first ad configuration and the second ad configuration, in a proportion based at least in part on a revenue amount associated with the bids to serve ads in the first ad configuration and bids to serve ads in the second ad configuration.

Abstract: Systems and methods are disclosed for attributing web traffic to an advertising spot. The method may include receiving traffic data for a web page from a server associated with an advertiser and receiving, from a log provider, a log of a plurality of advertising spots related to the advertiser. A duration of time as a peak may be designated to identify the amount of traffic that is attributable to the one of the plurality of advertising spots.

Abstract: 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.

Abstract: One or more computing devices, systems, and/or methods for selecting content items for transmission to client devices are provided. A request for content associated with a client device may be received. Bid values and/or click probabilities associated with content items may be determined. A probability of receiving a negative signal associated with a content item of the content items from the client device responsive to presenting the content item via the client device may be determined based upon a user profile associated with the client device. A content item score, of content item scores associated with the content items, may be generated based upon the probability, a click probability and/or a bid value associated with the content item. The content item may be selected from the content items for presentation via the client device based upon the content item scores. The content item may be transmitted to the client device.

Abstract: Systems and methods are provided for tracking sharing of an electronic content. An exemplary method may include receiving a request to access content associated with a web address by a user. Based on a unique identity assigned to the user and the web address, a unique tracking web address may be generated. This tracking web address may be shared with additional users. As other users request content associated with the tracking web address, information regarding the sharing of the electronic content may be determined and stored, allowing for tracking of sharing behavior of users.

Abstract: 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.

Abstract: Methods for creating and updating rules for distribution of an online advertising inventory. The methods can include generating a rule conditions section of a GUI that is configured to display a plurality of parameters of a rule condition of a rule for distribution of an online advertising inventory. Each displayed parameter of the rule condition can be graphically represented by a basic shape (e.g., a hexagon). And, each basic shape of the rule condition can be labeled with an alphanumerical indication of the graphically represented parameter and can also be clustered together in the GUI. The methods can also include generating a rule summary section of the GUI that is configured to display a plurality of rule conditions of a rule. The methods can also include generating a rules list section of the GUI that is configured to display a plurality of rules for distribution of inventory.