Abstract: A content analysis system includes processor and memory hardware storing data analyzed content items and instructions for execution by the processor hardware. The instructions include, in response to a first intermediate content item being analyzed to generate a first text description, receiving the first intermediate content item and analyzing the first text description to generate a first reduced text description. The instructions include identifying a first set of tags by applying a tag model to the first text description and generating a first analyzed content item. The instructions include adding the first analyzed content item to the analyzed content database and, in response to a displayed content item being associated with at least one tag of the first set of tags, displaying a first user-selectable link corresponding to the first analyzed content item on a portion of a user interface of a user device displaying the displayed content item.

Abstract: A content analysis system includes processor and memory hardware storing data analyzed content items and instructions for execution by the processor hardware. The instructions include, in response to a first intermediate content item being analyzed to generate a first text description, receiving the first intermediate content item and analyzing the first text description to generate a first reduced text description. The instructions include identifying a first set of tags by applying a tag model to the first text description and generating a first analyzed content item. The instructions include adding the first analyzed content item to the analyzed content database and, in response to a displayed content item being associated with at least one tag of the first set of tags, displaying a first user-selectable link corresponding to the first analyzed content item on a portion of a user interface of a user device displaying the displayed content item.

Abstract: A method of operating a customer utterance analysis system includes obtaining a subset of utterances from among a first set of utterances. The method includes encoding, by a sentence encoder, the subset of utterances into multi-dimensional vectors. The method includes generating reduced-dimensionality vectors by reducing a dimensionality of the multi-dimensional vectors. Each vector of the reduced-dimensionality vectors corresponds to an utterance from among the subset of utterances. The method includes performing clustering on the reduced-dimensionality vectors. The method includes, based on the clustering performed on the reduced-dimensionality vectors, arranging the subset of utterances into clusters. The method includes obtaining labels for a least two clusters from among the clusters. The method includes generating training data based on the obtained labels. The method includes training a neural network model to predict an intent of an utterance based on the training data.

Abstract: A method of operating a customer utterance analysis system includes obtaining a subset of utterances from among a first set of utterances. The method includes encoding, by a sentence encoder, the subset of utterances into multi-dimensional vectors. The method includes generating reduced-dimensionality vectors by reducing a dimensionality of the multi-dimensional vectors. Each vector of the reduced-dimensionality vectors corresponds to an utterance from among the subset of utterances. The method includes performing clustering on the reduced-dimensionality vectors. The method includes, based on the clustering performed on the reduced-dimensionality vectors, arranging the subset of utterances into clusters. The method includes obtaining labels for a least two clusters from among the clusters. The method includes generating training data based on the obtained labels. The method includes training a neural network model to predict an intent of an utterance based on the training data.

Abstract: A recommendation system includes a content similarity analyzer configured to determine a first set of content item identifiers similar to a set of viewed content items based on respective similarity scores and add them to a first list. A similar user content extraction module identifies a set of similar user identifiers from a user similarity database; obtains, based on respective viewing histories of the set, a second set of content item identifiers; and adds them to the first list. The recommendation system includes a content filter configured to select a subset of content item identifiers from the first list based on the corresponding similarity scores between content item identifiers of the first list and a viewing history. The content filter is configured to transmit the subset of content item identifiers for display of the corresponding content items via a web portal on a user interface of a first user device.

Abstract: A recommendation system includes a content similarity analyzer configured to determine a first set of content item identifiers similar to a set of viewed content items based on respective similarity scores and add them to a first list. A similar user content extraction module identifies a set of similar user identifiers from a user similarity database; obtains, based on respective viewing histories of the set, a second set of content item identifiers; and adds them to the first list. The recommendation system includes a content filter configured to select a subset of content item identifiers from the first list based on the corresponding similarity scores between content item identifiers of the first list and a viewing history. The content filter is configured to transmit the subset of content item identifiers for display of the corresponding content items via a web portal on a user interface of a first user device.

Abstract: A content analysis system includes processor and memory hardware storing data analyzed content items and instructions for execution by the processor hardware. The instructions include, in response to a first intermediate content item being analyzed to generate a first text description, receiving the first intermediate content item and analyzing the first text description to generate a first reduced text description. The instructions include identifying a first set of tags by applying a tag model to the first text description and generating a first analyzed content item. The instructions include adding the first analyzed content item to the analyzed content database and, in response to a displayed content item being associated with at least one tag of the first set of tags, displaying a first user-selectable link corresponding to the first analyzed content item on a portion of a user interface of a user device displaying the displayed content item.

Abstract: A content analysis system includes processor and memory hardware storing data analyzed content items and instructions for execution by the processor hardware. The instructions include, in response to a first intermediate content item being analyzed to generate a first text description, receiving the first intermediate content item and analyzing the first text description to generate a first reduced text description. The instructions include identifying a first set of tags by applying a tag model to the first text description and generating a first analyzed content item. The instructions include adding the first analyzed content item to the analyzed content database and, in response to a displayed content item being associated with at least one tag of the first set of tags, displaying a first user-selectable link corresponding to the first analyzed content item on a portion of a user interface of a user device displaying the displayed content item.

Abstract: A content analysis system includes processor and memory hardware storing data analyzed content items and instructions for execution by the processor hardware. The instructions include, in response to a first intermediate content item being analyzed to generate a first text description, receiving the first intermediate content item and analyzing the first text description to generate a first reduced text description. The instructions include identifying a first set of tags by applying a tag model to the first text description and generating a first analyzed content item. The instructions include adding the first analyzed content item to the analyzed content database and, in response to a displayed content item being associated with at least one tag of the first set of tags, displaying a first user-selectable link corresponding to the first analyzed content item on a portion of a user interface of a user device displaying the displayed content item.

Abstract: A method of operating a customer utterance analysis system includes obtaining a subset of utterances from among a first set of utterances. The method includes encoding, by a sentence encoder, the subset of utterances into multi-dimensional vectors. The method includes generating reduced-dimensionality vectors by reducing a dimensionality of the multi-dimensional vectors. Each vector of the reduced-dimensionality vectors corresponds to an utterance from among the subset of utterances. The method includes performing clustering on the reduced-dimensionality vectors. The method includes, based on the clustering performed on the reduced-dimensionality vectors, arranging the subset of utterances into clusters. The method includes obtaining labels for a least two clusters from among the clusters. The method includes generating training data based on the obtained labels. The method includes training a neural network model to predict an intent of an utterance based on the training data.

Abstract: A recommendation system includes a content similarity analyzer configured to determine a first set of content item identifiers similar to a set of viewed content items based on respective similarity scores and add them to a first list. A similar user content extraction module identifies a set of similar user identifiers from a user similarity database; obtains, based on respective viewing histories of the set, a second set of content item identifiers; and adds them to the first list. The recommendation system includes a content filter configured to select a subset of content item identifiers from the first list based on the corresponding similarity scores between content item identifiers of the first list and a viewing history. The content filter is configured to transmit the subset of content item identifiers for display of the corresponding content items via a web portal on a user interface of a first user device.

Abstract: A content recommendation system includes a processor and a memory coupled to the processor. The memory stores instructions that, upon execution, cause the processor to obtain a first viewing history of a first user from the viewing history index. The instructions include determining, based on the user similarity index, a first set of users similar to the first user. The instructions include obtaining a corresponding viewing history from the viewing history index and selecting a set of similar content item identifiers based on similarity scores. The instructions include updating a first recommendation list with (i) the corresponding viewing history for each similar user in the first set of users and (ii) the set of similar content item identifiers. The instructions include selecting and transmitting to a user device a subset of recommended content item identifiers from the first recommendation list.

Abstract: A content recommendation system includes a processor and a memory coupled to the processor. The memory stores instructions that, upon execution, causes the processor to obtain a first viewing history of a first user from the viewing history index. The instructions include determining, based on the user similarity index, a first set of users similar to the first user. The instructions include obtaining a corresponding viewing history from the viewing history index and selecting a set of similar content item identifiers from the plurality of content item identifiers based on similarity scores. The instructions include updating a first recommendation list with (i) the corresponding viewing history for each similar user in the first set of users and (ii) the set of similar content item identifiers. The instructions include selecting and transmitting to a user device a subset of recommended content item identifiers from the first recommendation list.