Abstract: A technique for dynamic digital ad configuration of digital ads being displayed on web pages and other digital properties is discussed. In one embodiment a site-specific Ad Tag is used to formulate a request for ad creatives for a publisher web page based on previously stored website configuration settings and dynamically retrieved targeting parameters. Ad creatives satisfying the request are identified and advertiser configuration settings for the identified and retrieved ad creative may be further adjusted based on the publisher website configuration settings before the ad creative is displayed on the website. Further, the site-specific Ad Tag may continue to monitor a performance of the displayed ad creative after the ad creative has been displayed on the web page of the publisher to optimize display and acquire information.

Abstract: Users may engage in a target user activity via digital systems, such as a website, and/or non-digital systems. Users may user various digital channels to arrive at the digital systems or non-digital systems. Users may also arrive at digital systems, such as the website, via different entry pages. A propensity analyzer can, based on activity data associated with users, determine propensities of one or more of the digital channels, digital systems, non-digital channels, and/or entry pages to drive users to perform a target user activity. The propensity analyzer can generate recommendations for revising digital channels, digital systems, non-digital channels, and/or entry pages to increase their propensities to drive users to perform the target user activity.

Abstract: An advertising campaign may be carried out using moving outdoor advertising. Advertisers typically like to manage and track the success of their advertising campaigns. Aspects of the present application may be seen to assist with such management and tracking by providing impression analytics, location analytics, time analytics and frequency reports. Furthermore, aspects of the present application may be seen to provide an ability to augment a moving outdoor advertising campaign with a mobile device retargeting advertising campaign. Then, such metrics as return on investment, profit and gross profit may be tracked. In some aspects, Wi-Fi signals and Bluetooth signals may be collected from mobile devices that are in the range of a Moving Impression Analytics Tracking Device.

Abstract: An example system includes: at least one memory; programmable circuitry; and instructions to cause the programmable circuitry to: obtain a public internet protocol (IP) address associated with a household and a timestamp in response to a census impression request indicating a media access on a first client device of the household; obtain a private IP address associated with the first client device; cause storing of the public IP address, the private IP address, and the timestamp in a panelist impression record in association with a panelist identifier, the panelist identifier corresponding to a panelist audience member, the panelist audience member enrolled in a panel of an audience measurement entity; send the census impression request to a census system; and send the panelist impression record to a meter collection system.

Abstract: A system provides for a client to receive enhancement data without having personally identifiable information leave its systems. The system receives access to a client configuration and a data graph to perform configuration defined filtering and aggregation steps to produce a set of client files. These files contain a hashed version of PII from the data graph. They are then used by the client to match the identity of its population of objects to keys, the keys also being included in the set of client files. The client associates corresponding keys with objects in its own data graph, then requests enhancement data using only the keys. The data is returned using the matched keys without the use of personally identifiable information.

Abstract: Implementations relate to training a model that can be used to process values for defined features, where the values are specific to a user account, to generate a predicted user measure that reflects both popularity and quality of the user account. The model is trained based on losses that are each generated as a function of both a corresponding generated popularity measure and a corresponding generated quality measure of a corresponding training instance. Accordingly, the model can be trained to generate, based on values for a given user account, a single measure that reflects both quality and popularity of the given user account. Implementations are additionally or alternatively directed to utilizing such predicted user measures to restrict provisioning of content items that are from user accounts having respective predicted user measures that fail to satisfy a threshold.

Abstract: A method of signaling between a photovoltaic module and an inverter module. The inverter module is connected to the photovoltaic module. In an initial mode of operation an initial code is modulated thereby producing an initial signal. The initial signal is transmitted from the inverter module to the photovoltaic module. The initial signal is received by the photovoltaic module. The operating mode is then changed to a normal mode of power conversion, and during the normal mode of operation a control signal is transmitted from the inverter to the photovoltaic module. A control code is demodulated and received from the control signal. The control code is compared with the initial code producing a comparison. The control command of the control signal is validated as a valid control command from the inverter module with the control command only acted upon when the comparison is a positive comparison.

Abstract: Systems and methods for determining emotional responses to videos, generating emotional arc based on the responses and reactions, and determining the type and/or point of insertion of secondary content based on the determined emotional responses are disclosed. The methods display primary content to a plurality of users and determine their emotional response as they consume the primary content. An average emotional arc is generated based on the determined emotional response and it is compared to a current emotional arc generated based on emotional response from a current user. When values of the current emotional are within a predefined range of the average emotional arc at a particular play position in the timeline of the primary content, a secondary content that is designated for the value of the current emotional arc is inserted in the primary content at the particular play position.

Abstract: Methods, apparatus, systems, and articles of manufacture are disclosed to extract information from uniform resource locators (URLs). An example system includes at least one memory, programmable circuitry, and instructions to cause the programmable circuitry to collect first uniform resource locator (URL) information from a server of an Internet-based media publisher, the first URL information corresponding to first media accessed by first users, determine first URL components in the first URL information, and determine feature-to-user assignment rules based on the first URL components.

Abstract: A method, a computer program product, and a system of training a machine learning model using federated learning with extreme gradient boosting. The method includes computing an epsilon hyperparameter using training dataset sizes from a first party and a second party. The method also includes transmitting a machine learning model and the epsilon hyperparameter to the first party and the second party and receiving a first model update and a second model update from the first party and the second party respectively. The method further includes fusing the first model update and the second model update to produce a global histogram and determining at least one split candidate in a decision tree used by the machine learning model using the global histogram. The method also includes rebuilding the machine learning model by adding the split candidate to a decision tree of the machine learning model.

Abstract: Aspects of the subject disclosure may include, for example, receiving a request to forecast allocations for a new descriptor, the new descriptor differing from preexisting descriptors, each of the preexisting descriptors being associated with a subset of locations of a network of locations, each location corresponding to an electronic segment in electronic canvases, and the new descriptor being associated with a new subset of locations of the network of locations, identifying one or more affected descriptors having one or more overlapping subsets of locations of the network of locations and one or more non-overlapping subsets of locations of the network of locations, determining a forecast of allocated locations in each of the one or more affected descriptors; identifying, according to the forecast, at least a portion of allocated locations in the one or more overlapping subsets of locations that are displaceable resulting in a number of displaceable allocations, and determining, according to the number of di

Abstract: Disclosed examples access media impression data via one or more wireless communications, the media impression data including panel data obtained from a meter and impression information obtained after an access of media at a computing device; determine an audience deduplication based on the panel data; determine odds ratios for platform combinations based on the audience deduplication; determine posterior distributions for the media based on the odds ratios; perform a sequential odds ratio insertion technique based on the posterior distributions to determine unique audience sizes; align the unique audience sizes based on a constraint; and generate a report including the aligned unique audience sizes.

Abstract: A method for facilitating serialized data interchange and access via an application programming interface is provided. The method includes retrieving a data set from a networked repository, the data set including information in a multidimensional data structure; mapping the data set to a predetermined file format; generating a standardized data set based on a result of the mapping, the standardized data set corresponding to the predetermined file format; persisting the standardized data set in the networked repository; and exposing the standardized data set in the networked repository via the application programming interface.

Abstract: Methods, systems, and computer programs encoded on a computer storage medium, for training and using machine learning models are disclosed. Methods include creating a model that represents relationships between user attributes, content exposures, and performance levels for a target action using organic exposure data specifying one or more organic exposures experienced by a particular user over a specified time prior to performance of a target action by the particular user and third party exposure data specifying third party exposures of a specified type of digital component to the particular user over the specified time period. Using the model, an incremental performance level attributable to each of the third party exposures at an action time when the target action was performed by the particular user is determined. Transmission criteria for at least some digital components to which the particular user was exposed are modified based on the incremental performance.

Abstract: Systems and methods are provided for electronic stimulus evaluation through one or more signals. The one or more signals protect underlying raw data relating to behavior, including purchase behavior and location-based behavior. A server platform in network communication with at least one signal buyer computer and at least one signal provider computer facilitates creation of the one or more signals. Anonymized identifiers are used to identify objects or consumers associated with behavior data and exposure to the electronic stimulus. The server platform provides optimization analytics for the stimulus based on the one or more signals, which include measurement signals, behavior signals, and lift signals.

Abstract: The present disclosure provides a video processing method and apparatus, a device, a computer readable storage medium, a computer program product, and a computer program, the method includes: acquiring an image to be processed; determining, according to the image to be processed, a target image and a target editing pattern, wherein the target editing pattern comprises at least one of a special effect acting on an audio corresponding to the target image, a special effect acting on an editing point between adjacent images, a special effect acting on a single image, and a special effect acting on an entire editing image; and performing, according to the target editing pattern, an editing operation on the target image, to obtain a target video.

Abstract: Systems and methods for computing a causal uplift in performance of an output action for one or more treatment actions in parallel are described herein. In an embodiment, a server computer receives interaction data for a particular period of time which identifies a plurality of users and a plurality of actions that were performed by each user of the plurality of users through a particular graphical user interface during the particular period of time. The server computer uses the interaction data to generate a feature matrix of actions for each user, and a set of confounding variables included to minimize spurious correlations. The feature matrix is then used to train a machine learning system, using data identifying a user's performance or non-performance of each action as inputs and data identifying performance or non-performance of a target output action as the output.

Abstract: This application relates to apparatus and methods for determining data outputs to advertise on a platform such as a website. A computing device receives a website search request and determines a search term keyword. The computing device also determines a plurality of item accounts, such as sponsor campaigns, based on the search term keyword and a corresponding keyword of each item account. Each item account also includes a corresponding data output, such as a digital advertisement. The computing device identifies one of the item accounts based on determining an engagement probability for the digital advertisement of each item account. The engagement probability is determined based on aggregated impression and engagement data for the digital advertisement. The computing device provides the digital advertisement for the identified item account to a server, which may display the digital advertisement on the platform.

Abstract: A verification system configured to determine when customers respond to a real-world ad unit is disclosed. The system comprises: a first camera, a second camera, and a processor. The first camera is mounted at a billboard promoting an associated merchant. The second camera is installed at the merchant where it is configured to capture images of people present at the merchant. The processor is configured to: receive images of a first set of people from the first camera at the billboard; receive images of a second set of people from the second camera at the merchant; and identify a subset of people that appear in the first set and then in the second set. The processor can then generate a conversion rate based on the first set of people and the third set of people. The advertising system is therefore a close-loop system for measuring the effectiveness of advertising.

Abstract: An online host for conducting a promotion campaign is presented. Online behaviors of subscribers exposed by the promotion campaign are tracked for determining conversion counts for the promotion campaign. For exposed subscribers whose online behaviors are not sufficiently available to the online host to determine conversion counts (non-measurable subscribers), a machine learning model is trained to predict conversion counts based on online behaviors that are conducted on internet locations under control of the online service, and further trained to determine an estimated error rate regarding the predicted conversion counts.

Abstract: Provided are an electronic device and an operating method thereof. The electronic device includes a communicator, a display, and a processor configured to receive search result data based on a search keyword from a first server through the communicator, request, from a second server through the communicator, additional information indicating whether each web page included in the search result data is an advertising page, and control the display to display the search result data, based on the additional information corresponding to the each web page and received from the second server, wherein the additional information is generated by the second server by using a training model trained using an artificial intelligence (AI) algorithm, based on whether the web page includes an advertisement pattern. Also provided are an AI system using a machine learning algorithm such as deep learning and applications thereof.

Abstract: A device may receive and transform metric data and share of voice data, associated with digital marketing by an entity, into transformed data, may generate model data from the transformed data, and may divide the model data into training data, test data, and validation data. The device may train models, with the training data, to generate training results, and may process the test data, with the models, to generate test results. The device may process the validation data, with the models, to generate validation results, and may select a first model, a second model, and a third model based on the results. The device may utilize the first model to predict a share of voice, and may utilize the second model to predict a click through rate. The device may utilize the third model to predict a conversion rate, and may perform actions based on the predicted data.

Abstract: Users may engage in a target user activity via digital systems, such as a website, and/or non-digital systems. Users may user various digital channels to arrive at the digital systems or non-digital systems. Users may also arrive at digital systems, such as the website, via different entry pages. A propensity analyzer can, based on activity data associated with users, determine propensities of one or more of the digital channels, digital systems, non-digital channels, and/or entry pages to drive users to perform a target user activity. The propensity analyzer can generate recommendations for revising digital channels, digital systems, non-digital channels, and/or entry pages to increase their propensities to drive users to perform the target user activity.

Abstract: Methods, apparatus, systems and articles of manufacture to expand panelist enrollment are disclosed. An example first media device includes processor circuitry to at least one of instantiate or execute the machine readable instructions to scan at least one local area network to which the first media device is connected, store an identification of a second media device connected to the at least one local area network, in response to scan of the at least one local area network indicating that the second media device is commonly connected to the at least one local area network with the first media device, add the identification of the second media device to a fingerprint, and transmit the fingerprint to a central facility.

Abstract: Aspects of the subject disclosure may include, for example, determining an ad play list for an Over-The-Top video stream requested by an end user device based on a price and/or a category of a creative derived from auctions with multiple Supply-Side Platform servers. The ad play list can be determined for the ad pod according to business rules, which can include a yield policy based on increasing revenue for a publisher of the video stream, and/or which can include a competitive separation policy enforced based on the category for bid responses. Other embodiments are disclosed.

Abstract: Methods, apparatus, systems, and articles of manufacture to perform computer-based monitoring of audiences of network-based media using information theory to estimate intermediate level unions are disclosed. An example apparatus to determine a deduplicated, census-based audience metric of media includes panel union calculator circuitry to calculate a threshold statistic corresponding to an intermediate union of a panel hierarchy, and census union calculator circuitry to calculate a deduplicated audience value corresponding to the intermediate union of the census hierarchy based on the threshold statistic.

Abstract: A method of generating an experience analytics script that is override-robust starts with processor receiving a script and generating, based on the script, an experience analytics script that is override-robust. Generating the experience analytics script comprises identifying expressions in the script referencing browser native functions of a browser, and replacing the expressions referencing the native functions of the browser with expressions including one or more references that are set at upon execution. The processor causes the experience analytics script to be executed on the browser of a customer client device. Upon execution of the experience analytics script, a value of the one or more references are set to an iframe window. The iframe window is associated with an iframe providing access to a pure untouched version of the native functions of the browser. Other embodiments are described herein.

Abstract: A system including one or more processors and one or more non-transitory computer-readable media storing computing instructions configured to run on the one or more processors and perform extracting meta features for an item to generate sparse feature embeddings for the item; reducing, using a multilayer perceptron, a dimension of the sparse feature embeddings to generate a representation vector for the meta features; extracting, using a recurrent neural network, sequential data from dense traffic features for the item over a period of time; and inputting the representation vector for the meta features and the sequential data from the dense traffic features into a multilayer neural network with a rectified linear unit (ReLU) activation function and a scoring layer to generate one or more performance metrics for the item. Other embodiments are disclosed.

Abstract: Approximating a more complex multi-objective feed item scoring model using a less complex single objective feed item scoring model in a multistage feed ranking system of an online service. The disclosed techniques can facilitate multi-objective optimization for personalizing and ranking feeds including balancing personalizing a feed for viewer experience, downstream professional or social network effects, and upstream effects on content creators. The techniques can approximate the multi-objective model—that uses a rich set of machine learning features for scoring feed items at a second pass ranker in the ranking system—with the more lightweight, single objective model—that uses fewer machine learning features at a first pass ranker in the ranking system. The single objective model can more efficiently score a large set of feed items while maintaining much of the multi-objective model's richness and complexity and with high recall at the second pass ranking stage.

Abstract: A software and/or hardware facility for scoring the quality of traffic to a site accessible via the Internet or other network. The facility may generate training set data and use the training set data to identify parameters indicative of fraudulent traffic to a site and reduce the effect of fraudulent traffic advertisers and publishers. The facility may score the quality of traffic and determine combinations of parameters that are indicative of the quality of traffic to the site. Traffic to the site may be scored based on the combination of parameters associated with the one or more sessions. Lower scores are indicative of traffic having little value to a publisher, advertiser, or third party; higher scores are indicative of traffic having greater value.

Abstract: Techniques for managing a multi-marketplace content presentation are described herein. For example, a computer system indicates, via a graphical user interface at a device associated with an account identifier, multiple web sites that offer an item for which content is to be presented. The computer system receives input indicating a selection of a set of web sites of the multiple web sites. The computer system receives a first control and a first target associated with presenting the content in the set of web sites and generates a second control and a second target. The computer system indicates the item and a first metric associated with presenting the content in the web sites based on the first control and the first target. The computer system indicates the selected web site and a second metric associated with presenting the content in the selected web site.

Abstract: Disclosed are embodiments for determining the impact of one or more latent factors on user interaction metrics based at least in part on an impact model. The embodiments identify a value for a user interaction metric, the user interaction metric measuring interaction with content and identify an impact for a latent factor on the user interaction metric, the impact determined based at least in part on a model providing a relationship between the user interaction metric and the latent factor. Additionally, embodiments may involve adjusting an attribute of the electronically provided content based at least in part on the impact of the latent factor on the user interaction metric.

Abstract: An advertising method, device and system, and a computer-readable storage medium are provided. The method includes: acquiring user information; sending the user information to a server that stores an advertising requirement, wherein the advertising requirement includes an advertisement content and an advertising condition of the advertisement content; receiving an advertising instruction of the advertisement content from the server, wherein the advertising instruction of the advertisement content is generated by the server when the user information meets the advertising condition of the advertisement content; and playing the advertisement content in response to the advertising instruction.

Abstract: Features are disclosed for training and using a recommendation model configured to receive inputs from a user via a first channel and a second channel. The recommendation model can be trained with inputs from the first channel and inputs from the second channel. The recommendation model can receive a plurality of inputs from multiple channels and determine a probability of a future potential event for each channel. The probabilities may be based on the probability of a repeat event and the probability of a general event. The recommendation model may generate a recommendation for the user based on the probabilities of a future potential event.

Abstract: Methods, apparatus, systems and articles of manufacture to expand panelist enrollment are disclosed. An example apparatus includes a network communicator to enable the apparatus to communicate with a local area network. A media monitor is to monitor a media presentation of the apparatus and report monitoring data identifying the media presentation to a central facility. A network scanner is to scan the local area network and store an identification of a second device connected to the same local area network as the apparatus. A network information processor is to review the identification of the second device connected to the same local area network as the apparatus, the network information processor to add the identification of the second device to a fingerprint if the second device is commonly connected to the same local area network as the apparatus. A fingerprint reporter is to transmit the fingerprint to the central facility.

Abstract: Aspects of the subject disclosure may include, for example, identifying user preferences for an XR application executing at an XR user system, wherein the user preferences are associated with an XR application user, accessing a historical profile associated with the XR application user, receiving local environment information from a sensor array of the XR user system, selecting an XR object for presentation on an XR display of the XR user system based on the local environment information, the user preferences, and the historical profile, and allocating compute resources to facilitate a rendering of the XR object, wherein the allocated compute resources are selected from a compute resource pool comprising local compute resources of the XR user system and edge compute resources of a network. Other embodiments are disclosed.

Abstract: Systems, methods, and computer-readable storage media that may be used to provide an adaptive user interface with a reduced data transmission payload are provided. One method includes identifying a plurality of data files available for transmission to a client device, each of the plurality of data files transmitted as a set of data packets having a payload comprising an amount of data, each of the plurality of data files having different payloads and causing the client device to display a respective user interface component within a user interface on the client device. The method further includes determining a property of the client device and/or a user associated with the client device and selecting a data file from among the plurality of data files in response to the property. The method further includes causing the selected data file to be transmitted to the client device.

Abstract: Methods, systems and computer program products implementing character-level deep neural networks for information extraction are disclosed. A system uses character-level information retrieved from a transaction record to classify the transaction as a whole and to tag individual sections of the transaction record by entity type. The system processes the transaction record using multiple and separate character-level models. The system can use a one-dimensional neural network for featurization fed into a fully connected network for classification for identifying the most common classes of a transaction record. The system can identify one or more entities, e.g., service provider names, from the transaction using an RNN. The RNN can include one or more LSTM models. The LSTM models can be BI-LSTM models.

Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods for customizing a set of restrictions for a hashtag or other user-defined annotation that violates guidelines or rules of an online resource based on automated and manual review. In particular, in one or more embodiments, the disclosed systems trigger manual review of user-defined annotations in a social networking system, determine various metrics based on both manual and automated review of content including a particular user-defined annotation, and generate a customized set of restrictions for the user-defined annotation based on those metrics. More specifically, the system can generate and utilize various manual review metrics and a moderated media metric to generate a custom set of restrictions for a user-defined annotation.

Abstract: A method for measuring effectiveness of a media-sharing advertisement includes: obtaining delivery destination ID identified in response to an access to a first address, which is based on a first code that is recorded on a print media and includes the first address to access to a first landing page corresponding to a first business entity of the print media and includes the delivery destination ID of the delivery destination of the print media; obtaining delivery destination ID identified in response to an access to a second address, which is based on a second code that is recorded on the print media and includes the second address to access to a second landing page corresponding to a second business entity that satisfies a media-sharing condition and includes the delivery destination ID, and ID of the second business entity; and providing the first and second business entities with the obtained information.

Abstract: An online system receives explicit user data and explicit event data, and implicit user data and implicit event data from a third party system. The online system generates an implicit users/implicit events data feature, an explicit users/explicit events data feature, and an explicit users/implicit events data feature. The online system generates a prediction of the counterfactual rate based on the implicit users/implicit events data feature, the explicit users/explicit events data feature, and the explicit users/explicit events data feature, the counterfactual rate indicating the likelihood that target users matching certain characteristics caused an event to occur when the target are not been presented with content by the online system, the content configured to induce users to cause the event to occur. A combined prediction rate is presented to the third party system based on the counterfactual rate.

Abstract: The present system, method, and media platform provides tools for increasing advertisement engagement with users by incentivizing engagement with various content displayed within a graphical user interface of a device by providing rewards for such engagement. Content engagement includes interacting with the displayed content or sharing such content on social media and the like, or by any other various action and/or input received from the user indicative of content engagement as defined in a set of parameters provided at least in part by the owners of the content. Content owners can purchase rewards which are selectively, as defined by parameters, distributed to one or more user rewards account upon receiving an input indicating a selection by the user of the content displayed. The user advantageously seeks out the content due, in part, to the rewards offer and relevance to the user's interests.

Abstract: Systems and methods are provided for controlling an online advertising campaign. In one embodiment, a computer-implemented method for controlling an online advertising campaign includes receiving a feedback signal reflecting delivery of the online advertising campaign, and comparing the feedback signal to a delivery reference to generate a campaign level control signal. The method further includes receiving a maximum impression bid price for an inventory unit of the online advertising campaign, the maximum bid price for the at least one inventory unit being set by a user, and calculating, using at least one processor, at least a final bid price based on the maximum bid price, on the campaign level control signal, and on an optimization objective for the online advertising campaign, the optimization objective being set by the user. The method also includes submitting, to an electronic market and based on the calculated final bid price, a bid on an impression from the inventory unit.

Abstract: Methods, apparatus, systems and articles of manufacture to monitor media presentations are disclosed. An example tangible computer readable storage medium includes instructions that, when executed, cause a machine to at least provide a panelist meter to a media device, the panelist meter including a web server to serve a panelist identifier on a local network of a device, provide a software development kit to a media provider, the software development kit to enable the media provider to create monitoring enabled media to request the panelist identifier from the web server of the panelist meter using a web protocol request, transmit the panelist identifier in response to a request from the panelist meter, the request including demographic information associated with a user of the media device, and collect media monitoring data from the monitoring enabled media.

Abstract: Users may arrive at a website via different digital channels. Users may also arrive at the website on different entry pages of the website. A propensity analyzer can, based on website activity data associated with a website, determine propensities of one or more of the digital channels, and/or one or more of the entry pages, to drive users to perform a target user activity during visits to the website. The propensity analyzer can generate recommendations for revising digital channels and/or entry pages to increase their propensities to drive users to perform the target user activity.

Abstract: Techniques of the disclosure are directed to sending and receiving messages that allow for content providers to interact with potential recipients and customers. An information distribution system may receive a first message from a content provider via a first client device that includes targeted content, such as a string of characters, an image, a video, and/or some other form of media. The targeted content may also include tags, such as hashtags, which identify or otherwise associate a particular topic with the user-generated content. Further, the targeted content may include a selectable graphical user interface element that a user reading the content may interact with on a second client device. If the user selects the selectable graphical user interface element, the information distribution system may generate a custom message that may be sent on behalf of the user, which may also include a tag, such as a hashtag.

Abstract: Systems and methods for enhancing virality for a content item are disclosed herein. A content item is uploaded to a content sharing platform over a communication network. Feedback on the content item is received from the content sharing platform over the communication network. Based on the feedback, a virality score for the content item is determined and a determination is made as to whether the virality score meets a virality criterion. In response to a determination that the virality score does not meet the virality criterion, a virality enhancement technique is selected from a virality enhancement database, the content item is modified by applying the virality enhancement technique to the content item, and the modified content item is uploaded to the content sharing platform over the communication network.

Abstract: Aspects of the subject disclosure may include, for example, receiving an indication of a selection of a first advertisement included within a first content item from a communication device, transmitting terms of purchase associated with the first advertisement responsive to the receiving of the indication of the selection of the first advertisement, selecting a second content item based on an indication of the first content item and/or a context associated with the communication device, transmitting the second content item to the communication device responsive to the selecting of the second content item, obtaining an indication of a transaction completed for a purchase from the communication device, selecting a second advertisement responsive to the indication of the transaction, and transmitting the second advertisement to the communication device. Other embodiments are disclosed.

Abstract: Systems and methods are described herein for updating search results based on a user's comment or a conversation among users using a media guidance application. A set of search results may be presented to a user or users. Comments or a conversation about the search results may be analyzed by the media guidance application. Selected comments by a user determined to have a greater weight may be used to update the search results.

Abstract: Embodiments of this application provide a method of distributing information from an allocator terminal to a client terminal over a communication network. Information allocation data in an information allocation system is collected by processing circuitry. The information allocation system is configured to allocate the information to a client terminal. The information allocation data includes data associated with a process of allocating the information. Data of actual effect and estimated effect of allocating the information is calculated according to the information allocation data. An allocation instruction is generated according to a comparison between the actual effect data and the corresponding estimated effect data. The allocation instruction is provided to the allocator terminal.