Abstract: This disclosure describes methods, systems, and non-transitory computer readable media for automatically parsing infographics into segments corresponding to structured groups or lists and displaying the identified segments or reflowing the segments into various computing tasks. For example, the disclosed systems may utilize a novel infographic grouping taxonomy and annotation system to group elements within infographics. The disclosed systems can train and apply a machine-learning-detection model to generate infographic segments according to the infographic grouping taxonomy. By generating infographic segments, the disclosed systems can facilitate computing tasks, such as converting infographics into digital presentation graphics (e.g., slide carousels), reflow the infographic into query-and-response models, perform search functions, or other computational tasks.

Abstract: Various embodiments are directed to assigning offers to marketing deliveries utilizing new features to describe offers in the marketing deliveries. Marketing deliveries can be described at a finer level to thus enhance the effectiveness of building and conducting marketing campaigns. The approaches facilitate matching content to recipients, predicting content performance, and measuring content performance after dispatching a marketing delivery.

Abstract: A method, apparatus, and non-transitory computer readable medium for chart question answering are described. The method, apparatus, and non-transitory computer readable medium may receive a text query about a chart, identify a plurality of chart elements in the chart, associate a text string from the text query with corresponding chart elements from the plurality of chart elements, replace the text string in the text query with arbitrary rare words based on the association to produce an encoded query, generate an embedded query based on the encoded query, generate an image feature vector based on the chart, combine the embedded query and the image feature vector to produce a combined feature vector, compute an answer probability vector based on the combined feature vector, and provide an answer to the text query based on the answer probability vector.

Abstract: Various embodiments are directed to assigning offers to marketing deliveries utilizing new features to describe offers in the marketing deliveries. Marketing deliveries can be described at a finer level to thus enhance the effectiveness of building and conducting marketing campaigns. The approaches facilitate matching content to recipients, predicting content performance, and measuring content performance after dispatching a marketing delivery.

Abstract: Embodiments are directed towards recommending and providing content to a user group for the social consumption of recommended content. Embodiments include generating an individual recommendation for the users based on user preferences of the plurality of users. A group recommendation is generated based on a combination of the individual recommendations. The group recommendation is provided to each of the plurality of users. The group may collaborate over the group recommendation. During collaboration, the group may generate a dialog that includes a discussion based on the group recommendation. Keyword and sentiment pairs, included in the dialog, are determined. The group recommendation is updated based on the keywords and sentiments. The updated group recommendation is provided to the users. In response to detecting a group consensus while monitoring the group collaboration, content associated with the group consensus is provided to the users.

Abstract: Techniques and systems for determining paywall metrics are described. In an implementation, a candidate paywall metric is created that corresponds to an increased propensity of users to engage in a paid transaction when exposed to a paywall. In this way, providers of digital content may increase the proportion of users that perform a transaction when exposed to the paywall.

Abstract: Techniques and systems for determining paywall metrics are described. In an implementation, a candidate paywall metric is created that corresponds to an increased propensity of users to engage in a paid transaction when exposed to a paywall. In this way, providers of digital content may increase the proportion of users that perform a transaction when exposed to the paywall.

Abstract: A natural language scene description is converted into a scene that is rendered in three dimensions by an augmented reality (AR) display device. Text-to-AR scene conversion allows a user to create an AR scene visualization through natural language text inputs that are easily created and well-understood by the user. The user can, for instance, select a pre-defined natural language description of a scene or manually enter a custom natural language description. The user can also select a physical real-world surface on which the AR scene is to be rendered. The AR scene is then rendered using the augmented reality display device according to its natural language description using 3D models of objects and humanoid characters with associated animations of those characters, as well as from extensive language-to-visual datasets. Using the display device, the user can move around the real-world environment and experience the AR scene from different angles.

Abstract: Multiple channels of audio/video (A/V) content in a system are available to users. A channel scoring system uses a user's channel watching behavior and channel browsing behavior to generate a channel preference score for various different channels in the system. The channel watching behavior refers to which channels the user watches. The channel browsing behavior refers to features describing the user's behavior when watching A/V content provided by a channel. Examples of these features include the hour of the day when the user watches the A/V content, the number and length of A/V content provided by the channel watched in a session, device preferences of the user, and so forth. Various actions can be taken based on the generated channel preference scores, such as recommending or otherwise promoting channels to the user.

Abstract: Various embodiments disambiguate users who share media content accounts to provide persona-based experience individualization. Personas correspond to commonly observed channel watching patterns among media content customers. Decomposition of the media content account into personas is achieved by analyzing many accounts, e.g., millions of accounts, on media content platforms. By analyzing accounts, a recommendation system can individualize the channel watching experience in media content accounts.

Abstract: Techniques are described for analyzing a video for memorability, identifying content features of the video that are likely to be memorable, and scoring specific content features within the video for memorability. The techniques can be optionally applied to selected features in the video, thus improving the memorability of the selected features. The features may be organic features of the originally captured video or add-in features provided using an editing tool. The memorability of video features, text features, or both can be improved by analyzing the effects of applying different styles or edits (e.g., sepia tone, image sharpen, image blur, annotation, addition of object) to the content features or to the video in general. Recommendations can then be provided regarding memorability score caused by application of the image styles to the video features.

Abstract: Certain embodiments involve generating summarized versions of video content based on memorability of the video content. For example, a video summarization system accesses segments of an input video. The video summarization system identifies memorability scores for the respective segments. The video summarization system selects a subset of segments from the segments based on each computed memorability score in the subset having a threshold memorability score. The video summarization system generates visual summary content from the subset of the segments.

Abstract: Embodiments are directed towards recommending and providing content to a user group for the social consumption of recommended content. Embodiments include generating an individual recommendation for the users based on user preferences of the plurality of users. A group recommendation is generated based on a combination of the individual recommendations. The group recommendation is provided to each of the plurality of users. The group may collaborate over the group recommendation. During collaboration, the group may generate a dialog that includes a discussion based on the group recommendation. Keyword and sentiment pairs, included in the dialog, are determined. The group recommendation is updated based on the keywords and sentiments. The updated group recommendation is provided to the users. In response to detecting a group consensus while monitoring the group collaboration, content associated with the group consensus is provided to the users.

Abstract: Techniques and systems for determining paywall metrics are described. In an implementation, a candidate paywall metric is created that corresponds to an increased propensity of users to engage in a paid transaction when exposed to a paywall. In this way, providers of digital content may increase the proportion of users that perform a transaction when exposed to the paywall.

Abstract: Various embodiments disambiguate users who share media content accounts to provide persona-based experience individualization. Personas correspond to commonly observed channel watching patterns among media content customers. Decomposition of the media content account into personas is achieved by analyzing many accounts, e.g., millions of accounts, on media content platforms. By analyzing accounts, a recommendation system can individualize the channel watching experience in media content accounts.

Abstract: Various embodiments are directed to assigning offers to marketing deliveries utilizing new features to describe offers in the marketing deliveries. Marketing deliveries can be described at a finer level to thus enhance the effectiveness of building and conducting marketing campaigns. The approaches facilitate matching content to recipients, predicting content performance, and measuring content performance after dispatching a marketing delivery.

Abstract: Techniques are described for analyzing a video for memorability, identifying content features of the video that are likely to be memorable, and scoring specific content features within the video for memorability. The techniques can be optionally applied to selected features in the video, thus improving the memorability of the selected features. The features may be organic features of the originally captured video or add-in features provided using an editing tool. The memorability of video features, text features, or both can be improved by analyzing the effects of applying different styles or edits (e.g., sepia tone, image sharpen, image blur, annotation, addition of object) to the content features or to the video in general. Recommendations can then be provided regarding memorability score caused by application of the image styles to the video features.

Abstract: Techniques are described for analyzing a video for memorability, identifying content features of the video that are likely to be memorable, and scoring specific content features within the video for memorability. The techniques can be optionally applied to selected features in the video, thus improving the memorability of the selected features. The features may be organic features of the originally captured video or add-in features provided using an editing tool. The memorability of video features, text features, or both can be improved by analyzing the effects of applying different styles or edits (e.g., sepia tone, image sharpen, image blur, annotation, addition of object) to the content features or to the video in general. Recommendations can then be provided regarding memorability score caused by application of the image styles to the video features.

Abstract: Techniques are described for analyzing a video for memorability, identifying content features of the video that are likely to be memorable, and scoring specific content features within the video for memorability. The techniques can be optionally applied to selected features in the video, thus improving the memorability of the selected features. The features may be organic features of the originally captured video or add-in features provided using an editing tool. The memorability of video features, text features, or both can be improved by analyzing the effects of applying different styles or edits (e.g., sepia tone, image sharpen, image blur, annotation, addition of object) to the content features or to the video in general. Recommendations can then be provided regarding memorability score caused by application of the image styles to the video features.

Abstract: Multiple channels of audio/video (A/V) content in a system are available to users. A channel scoring system uses a user's channel watching behavior and channel browsing behavior to generate a channel preference score for various different channels in the system. The channel watching behavior refers to which channels the user watches. The channel browsing behavior refers to features describing the user's behavior when watching A/V content provided by a channel. Examples of these features include the hour of the day when the user watches the A/V content, the number and length of A/V content provided by the channel watched in a session, device preferences of the user, and so forth. Various actions can be taken based on the generated channel preference scores, such as recommending or otherwise promoting channels to the user.