Abstract: Systems and methods for pulsing and controlling quality of content are provided. An automated QC system that automatically monitors (e.g., pulses) content for any changes by third-party servers and subsequently deactivates problematic content may improve user experience in relation to viewing content and enhance revenue gains for the content provider. For example, a confidence tool may identify problematic changes to the content via a pulsing mechanism, in which content is identified for deactivation until changes to the content meet the specification of the content presentation services. Active (e.g., live) or in-flight (e.g.

Abstract: Systems and methods for controlling quality of content are provided. Programmatic ad buying may facilitate and expedite ad buying via an automated process. However, quality control via a conventional ad buying process is done with manual intervention. A confidence tool may request, from a programmatic content library of a content provider, to analyze a content tag associated with programmatic content. The confidence tool may determine whether the content tag meets confidence criteria (e.g., specifications of a content presentation service). The confidence tool may notify the content provider of whether the content tag meets the confidence criteria. Based on this notification, the content provider may approve the content to be run or reject the content to prevent problematic content from running on the content presentation service.

Abstract: Systems and methods for controlling quality of content is provided. A confidence tool of an automated quality control system may receive a request to analyze a tag indicating content to be presented by a content presentation service. The tag may be indicative of a link to the content and a tracking pixel associated with the content. The confidence tool may determine whether the tag meets criteria (e.g., pixel whitelisting criteria, specification of a content presentation service). The confidence tool may notify a user whether the tag meets the criteria to prevent problematic content from being presented by the content presentation service.

Abstract: Systems and methods for controlling quality of content are provided. Programmatic ad buying may facilitate and expedite ad buying via an automated process. However, quality control via a conventional ad buying process is done with manual intervention. A confidence tool may request, from a programmatic content library of a content provider, to analyze a content tag associated with programmatic content. The confidence tool may determine whether the content tag meets confidence criteria (e.g., specifications of a content presentation service). The confidence tool may notify the content provider of whether the content tag meets the confidence criteria. Based on this notification, the content provider may approve the content to be run or reject the content to prevent problematic content from running on the content presentation service.

Abstract: Systems and methods for controlling quality of content is provided. A confidence tool of an automated quality control system may receive a request to analyze a tag indicating content to be presented by a content presentation service. The tag may be indicative of a link to the content and a tracking pixel associated with the content. The confidence tool may determine whether the tag meets criteria (e.g., pixel whitelisting criteria, specification of a content presentation service). The confidence tool may notify a user whether the tag meets the criteria to prevent problematic content from being presented by the content presentation service.

Abstract: Systems and methods for controlling quality of content is provided. A confidence tool of an automated quality control system may receive a request to analyze a tag indicating content to be presented by a content presentation service. The tag may be indicative of a link to the content and a tracking pixel associated with the content. The confidence tool may determine whether the tag meets criteria (e.g., pixel whitelisting criteria, specification of a content presentation service). The confidence tool may notify a user whether the tag meets the criteria to prevent problematic content from being presented by the content presentation service.

Abstract: Systems and methods for pulsing and controlling quality of content are provided. An automated QC system that automatically monitors (e.g., pulses) content for any changes by third-party servers and subsequently deactivates problematic content may improve user experience in relation to viewing content and enhance revenue gains for the content provider. For example, a confidence tool may identify problematic changes to the content via a pulsing mechanism, in which content is identified for deactivation until changes to the content meet the specification of the content presentation services. Active (e.g., live) or in-flight (e.g.

Abstract: A “Facet Recommender” creates conversational recommendations for facets of particular conversational topics, and optionally for things associated with those facets, from consumer reviews or other social media content. The Facet Recommender applies a machine-learned facet model and optional sentiment-model, to identify facets associated with spans or segments of the content and to determine neutral, positive, or negative consumer sentiment associated with those facets and, optionally, things associated with those facets. These facets are selected by the facet model from a list or set of manually defined or machine-learned facets for particular conversational topic types. The Facet Recommender then generates new conversational utterances (i.e., short neutral, positive or negative suggestions) about particular facets based on the sentiments associated with those facets. In various implementations, utterances are fit to one or more predefined conversational frameworks.

Abstract: A “Facet Recommender” creates conversational recommendations for facets of particular conversational topics, and optionally for things associated with those facets, from consumer reviews or other social media content. The Facet Recommender applies a machine-learned facet model and optional sentiment-model, to identify facets associated with spans or segments of the content and to determine neutral, positive, or negative consumer sentiment associated with those facets and, optionally, things associated with those facets. These facets are selected by the facet model from a list or set of manually defined or machine-learned facets for particular conversational topic types. The Facet Recommender then generates new conversational utterances (i.e., short neutral, positive or negative suggestions) about particular facets based on the sentiments associated with those facets. In various implementations, utterances are fit to one or more predefined conversational frameworks.

Abstract: A “Facet Recommender” creates conversational recommendations for facets of particular conversational topics, and optionally for things associated with those facets, from consumer reviews or other social media content. The Facet Recommender applies a machine-learned facet model and optional sentiment-model, to identify facets associated with spans or segments of the content and to determine neutral, positive, or negative consumer sentiment associated with those facets and, optionally, things associated with those facets. These facets are selected by the facet model from a list or set of manually defined or machine-learned facets for particular conversational topic types. The Facet Recommender then generates new conversational utterances (i.e., short neutral, positive or negative suggestions) about particular facets based on the sentiments associated with those facets. In various implementations, utterances are fit to one or more predefined conversational frameworks.

Abstract: A “Facet Recommender” creates conversational recommendations for facets of particular conversational topics, and optionally for things associated with those facets, from consumer reviews or other social media content. The Facet Recommender applies a machine-learned facet model and optional sentiment-model, to identify facets associated with spans or segments of the content and to determine neutral, positive, or negative consumer sentiment associated with those facets and, optionally, things associated with those facets. These facets are selected by the facet model from a list or set of manually defined or machine-learned facets for particular conversational topic types. The Facet Recommender then generates new conversational utterances (i.e., short neutral, positive or negative suggestions) about particular facets based on the sentiments associated with those facets. In various implementations, utterances are fit to one or more predefined conversational frameworks.