Abstract: Embodiments are directed towards determining an overall success of a plurality of features and recommending a subset of those features to a user for generating new content. For each feature that is associated with content, an overall success may be determined based on a success of each of a plurality of content that the feature is associated. A subset of the features may be displayed to the user—from which the user can select features to utilize for generating the new content. Based on the features selected by the user, the displayed features may be dynamically updated to include one or more recommended features. A feature may be recommended to the user based on a combination of the overall success of each selected feature and the overall success of each recommended feature. The selected features may be utilized to create the new content.

Abstract: Embodiments are directed towards personalizing content to be provided to a user. A recommendation score may be determined for a piece of content for a user. The recommendation score may be based on a combination of an intrinsic value and/or a social value of the content to the user. The social value may be calculated based on a combination of an individual social value for each of the user's friends, which may be determined based on the combination of a social weight, an interest probability, and a recommendation score for the friend. Online personalization of content for a user may provide the user with the tools to be a valuable, appreciated member of the user's social network. By employing embodiments, as described herein, content may be determined and provided to a user, which may help the user gain the attention of their social circles.

Abstract: Embodiments are directed towards modifying when to publish content to a channel for distribution based on monitored actions associated with previously published content. A plurality of publishing slots may be determined from a plurality of time slots. Each publishing slot may indicate a time to publish content to the channel. Content may be published to the channel at each of the plurality of publishing slots. User actions associated with the published content may be monitored. The plurality of publishing slots may be ranked based on these monitored actions. At least one of the plurality of publishing slots may be modified to a different time slot based on the rankings of the publishing slots. The publication of content at a publishing slot may be delayed based on a success of other content published at a previous publishing slot being above a threshold.

Abstract: Embodiments are directed towards personalizing content to be provided to a user. A recommendation score may be determined for a piece of content for a user. The recommendation score may be based on a combination of an intrinsic value and/or a social value of the content to the user. The social value may be calculated based on a combination of an individual social value for each of the user's friends, which may be determined based on the combination of a social weight, an interest probability, and a recommendation score for the friend. Online personalization of content for a user may provide the user with the tools to be a valuable, appreciated member of the user's social network. By employing embodiments, as described herein, content may be determined and provided to a user, which may help the user gain the attention of their social circles.

Abstract: Embodiments are directed towards modifying when to publish content to a channel for distribution based on monitored actions associated with previously published content. A plurality of publishing slots may be determined from a plurality of time slots. Each publishing slot may indicate a time to publish content to the channel. Content may be published to the channel at each of the plurality of publishing slots. User actions associated with the published content may be monitored. The plurality of publishing slots may be ranked based on these monitored actions. At least one of the plurality of publishing slots may be modified to a different time slot based on the rankings of the publishing slots. The publication of content at a publishing slot may be delayed based on a success of other content published at a previous publishing slot being above a threshold.

Abstract: Embodiments are directed towards dynamically managing contextual recommendations that can be employed with content when provided to a channel. A test for a variable having at least two outcomes may be determined to be employed with selected content. Each outcome may correspond to a different contextual recommendation. Each selected content may be published to the channel at least twice, where a portion of the selected content may be initially published based on a contextual recommendation corresponding to a first outcome and later republished based on another contextual recommendation corresponding to the second outcome, and a separate portion of the selected content may be initially published based on the other contextual recommendation corresponding to the second outcome and later republished based on the contextual recommendation corresponding to the first outcome. A preferred outcome for the test may be determined based on user actions associated with the published selected content.

Abstract: Embodiments are directed towards modifying when to publish content to a channel for distribution based on monitored actions associated with previously published content. A plurality of publishing slots may be determined from a plurality of time slots. Each publishing slot may indicate a time to publish content to the channel. Content may be published to the channel at each of the plurality of publishing slots. User actions associated with the published content may be monitored. The plurality of publishing slots may be ranked based on these monitored actions. At least one of the plurality of publishing slots may be modified to a different time slot based on the rankings of the publishing slots. The publication of content at a publishing slot may be delayed based on a success of other content published at a previous publishing slot being above a threshold.

Abstract: Embodiments are directed towards personalizing content to be provided to a user. A recommendation score may be determined for a piece of content for a user. The recommendation score may be based on a combination of an intrinsic value and/or a social value of the content to the user. The social value may be calculated based on a combination of an individual social value for each of the user's friends, which may be determined based on the combination of a social weight, an interest probability, and a recommendation score for the friend. Online personalization of content for a user may provide the user with the tools to be a valuable, appreciated member of the user's social network. By employing embodiments, as described herein, content may be determined and provided to a user, which may help the user gain the attention of their social circles.

Abstract: Embodiments are directed towards dynamically managing contextual recommendations that can be employed with content when provided to a channel. A test for a variable having at least two outcomes may be determined to be employed with selected content. Each outcome may correspond to a different contextual recommendation. An outcome to employ with the content may be randomly determined/selected such that each of the outcomes of the test is selected a substantially similar number of times. The content may be modified and/or provided to the channel based on contextual recommendations that correspond to the determined outcome. Actions associated with a plurality of content may be monitored and/or analyzed to determine metrics for each outcome of a test. A comparison of the metrics for each outcome may be utilized to determine a preferred outcome for the test.

Abstract: Embodiments are directed towards dynamically managing a plurality of channels for providing content to users. New channels may be automatically generated based on sub-interest groups of existing channels, which may generate a hierarchy of channels, where new content may be posted and/or provided to the new channel, and/or a parent channel. The new channel may be proposed to a user, that accesses content on a channel, to invite the user to subscribe to the new channel. The proposed channel may be determined based on a weight of channels associated with the accessed content. Similarly, a proposed channel may be provided to a publisher for new content based on the weight of channels associated with the new content. Channels may be discontinued if a value of the channel falls below a predetermined threshold. Channels with overlapping characteristics may be merged together if the overlapping channels satisfy merging criteria.

Abstract: Embodiments are directed towards personalizing content to be provided to a user. A recommendation score may be determined for a piece of content for a user. The recommendation score may be based on a combination of an intrinsic value and/or a social value of the content to the user. The social value may be calculated based on a combination of an individual social value for each of the user's friends, which may be determined based on the combination of a social weight, an interest probability, and a recommendation score for the friend. Online personalization of content for a user may provide the user with the tools to be a valuable, appreciated member of the user's social network. By employing embodiments, as described herein, content may be determined and provided to a user, which may help the user gain the attention of their social circles.

Abstract: Embodiments are directed towards modifying when to publish content to a channel for distribution based on monitored actions associated with previously published content. A plurality of publishing slots may be determined from a plurality of time slots. Each publishing slot may indicate a time to publish content to the channel. Content may be published to the channel at each of the plurality of publishing slots. User actions associated with the published content may be monitored. The plurality of publishing slots may be ranked based on these monitored actions. At least one of the plurality of publishing slots may be modified to a different time slot based on the rankings of the publishing slots. The publication of content at a publishing slot may be delayed based on a success of other content published at a previous publishing slot being above a threshold.

Abstract: Embodiments are directed towards dynamically managing contextual recommendations that can be employed with content when provided to a channel. A test for a variable having at least two outcomes may be determined to be employed with selected content. Each outcome may correspond to a different contextual recommendation. An outcome to employ with the content may be randomly determined/selected such that each of the outcomes of the test is selected a substantially similar number of times. The content may be modified and/or provided to the channel based on contextual recommendations that correspond to the determined outcome. Actions associated with a plurality of content may be monitored and/or analyzed to determine metrics for each outcome of a test. A comparison of the metrics for each outcome may be utilized to determine a preferred outcome for the test.

Abstract: Technologies are described herein for suggesting long-tail tags. A first group of tags and a second group of tags are identified from a plurality of tags. The first group of tags includes frequently-assigned tags having a higher frequency of being assigned to an asset. The second group of tags includes long-tail tags having a lower frequency of being assigned to the asset than the frequently-assigned tags. The frequently-assigned tags and a sample of the long-tail tags are suggested to a user upon receiving a request from the user to tag the asset.

Abstract: Methods, systems, and computer-readable media for finding similarities between visual objects using keywords and computerized visual image analysis are provided. A visual object may be provided as an input. A group of visual objects sharing keywords with the visual object may be generated for further analysis. The visual similarity of this group of visual objects may then be determined using computerized visual analysis. A group of visual objects that have the highest similarity rank, as determined by the computerized visual analysis, may then be displayed.

Abstract: Technologies are described herein for suggesting long-tail tags. A first group of tags and a second group of tags are identified from a plurality of tags. The first group of tags includes frequently-assigned tags having a higher frequency of being assigned to an asset. The second group of tags includes long-tail tags having a lower frequency of being assigned to the asset than the frequently-assigned tags. The frequently-assigned tags and a sample of the long-tail tags are suggested to a user upon receiving a request from the user to tag the asset.