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: Disclosed are systems and methods for improving interactions with and between computers in content generating, searching, hosting and/or providing systems supported by or configured with personal computing devices, servers and/or platforms. The systems interact to identify and retrieve data within or across platforms, which can be used to improve the quality of data used in processing interactions between or among processors in such systems. The disclosed systems and methods automatically analyze a live streaming media file, and identify portions of the media that are highlights. The content classified as a highlight can be shared across social media platforms, and indexed for searching respective to attributes of the video content. The streaming and highlight media content is renderable in a novel, modified video player that enables variable playback speeds for how content is classified, and enables on-demand selections of specific content portions and adjustable rendering displays during streaming.

Abstract: Systems, apparatuses, and methods are provided for determining a bid value for placing an advertisement onto advertising space available through an electronic marketplace. A method is used for calculating the option value of maintaining the advertisement in the advertising space during one or more periods of time. The option value may be based on expected profits and the estimated future value of maintaining the advertisement. The option value may then be used to calculate the bid price for placing the advertisement.

Abstract: One or more computing devices, systems, and/or methods are provided. In an example, a review initiation request may be received from a first email account. The review initiation request may indicate proposed email content and/or a reviewer of the proposed email content. An email may be generated based upon the review initiation request. The email may include the proposed email content and/or an email review interface including a feedback entry field and/or an approval selectable input. The email may be transmitted to a reviewer email account of the reviewer. A review response may be received, via the email review interface, from the reviewer email account. The review response may indicate (i) feedback, indicating one or more suggestions associated with the proposed email content, submitted via the feedback entry field, and/or (ii) an approval indicator, indicating approval of the proposed email content, submitted via the approval selectable input.

Abstract: One or more computing devices, systems, and/or methods for future event recommendation are provided. For example, messages of a user account of a user are parsed to identify messages corresponding to events attended by the user. Event features are extracted from the messages, where an event feature is descriptive of an aspect of an event. An event database of future events is queried using the event features to identify future events having one or more of the event features. A description of the future events is constructed and provided to the user.

Abstract: In an aspect, fantasy sports data is analyzed to generate and/or manage the delivery of and user experience of fantasy sports games and contests, including timing and payout structure(s) for short-term fantasy contests, such as daily, weekly, etc. fantasy sports contests, that motivate and appeal to contestants and is/are reasonably structured and concisely represented. In yet another aspect, a daily fantasy contest system is provided to automatically adjust to user demand without overshooting profitable levels and offer a variety of different contests, as well as offer contests with guaranteed and non-guaranteed payouts.

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 messaging framework that automatically applies a multi-factor analysis technique to incoming and received messages in order to properly identify a message's type and category, which dictates the manner in which the message is displayed within a recipient's inbox. The disclosed framework operates on two levels: i) it determines whether a message is from a human or machine sender (H/M classification), and ii) it determines the messages category (MAGMA categorization).

Abstract: Devices often receive content items to be saved for later presentation to a user. In some instances, the content of the message is analyzed to retrieve supplemental information from external data sources, such as a map to a location discussed in a message. However, such supplementing often fails to reflect the personal context of the content item to the user, who may not easily recall the associated personal context when the content item is later presented. Instead, for a content item to be saved, a device may evaluate personal data sources to identify the personal context of the content item to the user, and add personal context annotations to a content item record of the saved content item. Later, during a presentation of the content item to the user, the personal context annotations may be included as a reflection of the personal context of the content item to the user.

Abstract: The present teaching relates to method, system, medium, and implementations for machine learning. Upon receiving input data associated with a time series, hidden representations associated with the time series in a feature space are obtained and used to generate a query vector in a query space. Such generated query vector is then used to query relevant historic information related to the time series. The query vector and the relevant historic information are aggregated to generate at least one queried vector, which is aggregated with the hidden representations to generate enriched hidden representations that enhance the expressiveness of the hidden representations.

Abstract: Disclosed are systems and methods for improving interactions with and between computers in electronic messaging, and other, systems supported by or configured with personal computing devices, servers and/or platforms. The systems interact to identify and retrieve data within or across platforms, which can be used to improve the quality of data used in processing interactions between or among processors in such systems. The disclosed systems and methods provide systems and methods for automatically generating data extraction rules, which can then be used to automatically extract data from electronic messages.

Abstract: 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.

Abstract: A method of generating congruous metadata is provided. The method includes receiving a similarity measure between at least two multimedia objects. Each multimedia object has associated metadata. If the at least two multimedia objects are similar based on the similarity measure and a similarity threshold, the associated metadata of each of the multimedia objects are compared. Then, based on the comparison of the associated metadata of each of the at least two multimedia objects, the method further includes generating congruous metadata. Metadata may be tags, for example.

Abstract: The disclosed systems and methods provide a framework for a proactive prediction of the toxic propensity of an article. Prior to the publication and/or reception of comments to online content, the disclosed framework determines the toxic propensity of the content's context and/or specific words, sentences, sentiments, tone or other messages receivable from consumption of the content. Thus, disclosed framework performs proactive forecasting of the content's toxicity propensity”, which quantifies how likely the content is prone to incur or attract toxic comments. The framework can function and/or be configured to operate in a manner that can perform specifically adherent moderation actions that correspond to the content and control how the content can be interacted with, based on the toxic propensity determination, prior to the content's publication in an effort to thwart, prevent or stop toxic environments surrounding or stemming from the content from coming into existence.

Abstract: One or more computing devices, systems, and/or methods for content recommendations using historical future data are provided. A model serving delay time is computed as an average of training delays of events. A historical data time interval is determined based upon the model serving delay time. A model is trained for predicting user content preferences using historic user distribution data and historic content distribution data associated with the historic data time interval. The model is utilized to generate and provide content recommendations to users.

Abstract: The present disclosure relates to systems and methods customizing electronic communications. A future event associated with a first user may be determined, and a second user that is associated with the first user may be identified. A plurality of communications involving the first user and the second user may be analyzed. A selection rule may be applied based on the analyzed plurality of communications, the selection rule identifying content from the database. Content from the database may be selected based on the application of the selection rule. An electronic message may be provided to the first user identifying the future event, and the selected content may be provided to the first user.

Abstract: The present teaching relates to method, system, medium, and implementations for machine learning. Machine learning is performed based on training data via a dual loop learning process that includes a first loop for data decoding learning and a second loop for label decoding learning. In the first loop, first parameters associated with decoding are updated to generate updated first parameters based on a first label, estimated via the decoding using the first parameters, and a second label, predicted via the label decoding using second parameters. In the second loop, the second parameters associated with the label decoding are updated to generate updated second parameters based on a third label, obtained via the decoding using the updated first parameters, and a ground truth label.

Abstract: Techniques for electronic document content bookmarking are disclosed. In one embodiment, a method is disclosed comprising receiving user input indicating selection of a portion of the electronic document, receiving a request to bookmark the selected portion of the electronic document, generating a bookmark card for the selected portion of the electronic document, the bookmark card comprising information identifying the electronic document, the selected portion of the electronic document as bookmarked content and the at least one user-defined information item, receiving, via the computing device, a bookmark view request via the GUI, and causing display of the bookmark view via the GUI in response to the bookmark view request, the bookmark view comprising an entry corresponding to the generated bookmark card and providing the bookmarked content from the generated bookmark card, the bookmark view providing a number of actions selectable by the user in connection with the entry from the bookmark view.

Abstract: Systems and methods are for identifying online advertisements to display to a user based on historical user data collected across the user's use of a plurality of Internet devices. One method includes receiving, over a network, a request for an advertisement to display at a first device of the user, the request including a unique identifier stored on the user's first device; accessing, in a database, a demographic or browsing history information generated from the user's use of a second device, the demographic or browsing history information being stored in the database in relation to the unique identifier; and identifying, based on the demographic or browsing history information, an advertisement to display at the user's first device. The demographic or browsing history information is synchronized based on the unique identifier being stored on the user's first device and the user's second device.

Abstract: Disclosed are systems and methods for improving interactions with and between computers in content generating, searching, hosting and/or providing systems supported by or configured with personal computing devices, servers and/or platforms. The systems interact to identify and retrieve data within or across platforms, which can be used to improve the quality of data used in processing interactions between or among processors in such systems. The disclosed systems and methods provide systems and methods for privacy-aware machine learning using an improved data encoding that withholds more information. The disclosed systems and methods encode a corpus of data and encode each query used in searching and generating query results from the corpus of encoded data.

Abstract: In some aspects, the techniques described herein relate to a method including: receiving a set of messages; filtering the set of messages to identify a set of newsletters in response to a selection of a newsletter control within a messaging application; rendering a newsletter view within the messaging application, the newsletter view displaying a plurality of tiles corresponding to the set of newsletters; receiving a section of a given tile in the plurality of tiles from a user; and rendering a newsletter reader view, the newsletter reader view including a subject and body of a selected newsletter corresponding to the given tile.