Abstract: Techniques, systems, and methods are disclosed to generate messaging to view content on media devices based on predictive factors. Information may be received to trigger one or more predictive factors and then generate a candidate set of offers to view content at a media device based on the information. Based on the one or more predictive factors, confidence values may be determined for each offer in the candidate set of offers. The candidate set of offers may be ranked based on the associated confidence values. Subsequently, presentation of at least one offer of the candidate set of offers may be caused to display in a user interface screen on the media device based on the ranking.

Abstract: Methods and system for determining a similarity relationship between a plurality of digital assets and a target digital asset comprises creating a normalized semantic feature vector associated with a search query, discovering the target asset based on the normalized semantic feature vector, generating a normalized latent feature vector associated with the target asset, comparing the normalized semantic feature vector with semantic feature vectors for each of the digital assets to generate a semantic comparison value, comparing the normalized target latent feature vector with latent feature vectors for each of the digital assets to generate a latent comparison value, blending the semantic comparison vector value with the latent feature comparison vector value to create a target comparison value for each of the digital assets, and reporting the digital assets having the highest target comparison values to the user or group of users.

Abstract: Techniques, systems, and methods are disclosed to generate messaging to view content on media devices based on predictive factors. Information may be received to trigger one or more predictive factors and then generate a candidate set of offers to view content at a media device based on the information. Based on the one or more predictive factors, confidence values may be determined for each offer in the candidate set of offers. The candidate set of offers may be ranked based on the associated confidence values. Subsequently, presentation of at least one offer of the candidate set of offers may be caused to display in a user interface screen on the media device based on the ranking.

Abstract: Methods and system for determining a similarity relationship between a plurality of digital assets and a target digital asset comprises creating a normalized semantic feature vector associated with a search query, discovering the target asset based on the normalized semantic feature vector, generating a normalized latent feature vector associated with the target asset, comparing the normalized semantic feature vector with semantic feature vectors for each of the digital assets to generate a semantic comparison value, comparing the normalized target latent feature vector with latent feature vectors for each of the digital assets to generate a latent comparison value, blending the semantic comparison vector value with the latent feature comparison vector value to create a target comparison value for each of the digital assets, and reporting the digital assets having the highest target comparison values to the user or group of users.

Abstract: Methods and system for determining a similarity relationship between a plurality of digital assets and a target digital asset comprises creating a normalized semantic feature vector associated with a search query, discovering the target asset based on the normalized semantic feature vector, generating a normalized latent feature vector associated with the target asset, comparing the normalized semantic feature vector with semantic feature vectors for each of the digital assets to generate a semantic comparison value, comparing the normalized target latent feature vector with latent feature vectors for each of the digital assets to generate a latent comparison value, blending the semantic comparison vector value with the latent feature comparison vector value to create a target comparison value for each of the digital assets, and reporting the digital assets having the highest target comparison values to the user or group of users.

Abstract: Methods and system for determining a similarity relationship between a plurality of digital assets and a target digital asset comprises creating a normalized semantic feature vector associated with a search query, discovering the target asset based on the normalized semantic feature vector, generating a normalized latent feature vector associated with the target asset, comparing the normalized semantic feature vector with semantic feature vectors for each of the digital assets to generate a semantic comparison value, comparing the normalized target latent feature vector with latent feature vectors for each of the digital assets to generate a latent comparison value, blending the semantic comparison vector value with the latent feature comparison vector value to create a target comparison value for each of the digital assets, and reporting the digital assets having the highest target comparison values to the user or group of users.

Abstract: Techniques, systems, and methods are disclosed to generate messaging to view content on media devices based on predictive factors. Information may be received to trigger one or more predictive factors and then generate a candidate set of offers to view content at a media device based on the information. Based on the one or more predictive factors, confidence values may be determined for each offer in the candidate set of offers. The candidate set of offers may be ranked based on the associated confidence values. Subsequently, presentation of at least one offer of the candidate set of offers may be caused to display in a user interface screen on the media device based on the ranking.

Abstract: Techniques, systems, and methods are disclosed to generate messaging to view content on media devices based on predictive factors. Information may be received to trigger one or more predictive factors and then generate a candidate set of offers to view content at a media device based on the information. Based on the one or more predictive factors, confidence values may be determined for each offer in the candidate set of offers. The candidate set of offers may be ranked based on the associated confidence values. Subsequently, presentation of at least one offer of the candidate set of offers may be caused to display in a user interface screen on the media device based on the ranking.

Abstract: Methods and system for determining a similarity relationship between a plurality of digital assets and a target digital asset comprises creating a normalized semantic feature vector associated with a search query, discovering the target asset based on the normalized semantic feature vector, generating a normalized latent feature vector associated with the target asset, comparing the normalized semantic feature vector with semantic feature vectors for each of the digital assets to generate a semantic comparison value, comparing the normalized target latent feature vector with latent feature vectors for each of the digital assets to generate a latent comparison value, blending the semantic comparison vector value with the latent feature comparison vector value to create a target comparison value for each of the digital assets, and reporting the digital assets having the highest target comparison values to the user or group of users.

Abstract: Methods and system for determining a similarity relationship between a plurality of digital assets and a target digital asset comprises creating a normalized semantic feature vector associated with a search query, discovering the target asset based on the normalized semantic feature vector, generating a normalized latent feature vector associated with the target asset, comparing the normalized semantic feature vector with semantic feature vectors for each of the digital assets to generate a semantic comparison value, comparing the normalized target latent feature vector with latent feature vectors for each of the digital assets to generate a latent comparison value, blending the semantic comparison vector value with the latent feature comparison vector value to create a target comparison value for each of the digital assets, and reporting the digital assets having the highest target comparison values to the user or group of users.

Abstract: Techniques, systems, and methods are disclosed to generate messaging to view content on media devices based on predictive factors. Information may be received to trigger one or more predictive factors and then generate a candidate set of offers to view content at a media device based on the information. Based on the one or more predictive factors, confidence values may be determined for each offer in the candidate set of offers. The candidate set of offers may be ranked based on the associated confidence values. Subsequently, presentation of at least one offer of the candidate set of offers may be caused to display in a user interface screen on the media device based on the ranking.

Abstract: Methods and system for determining a similarity relationship between a plurality of digital assets and a target digital asset comprises creating a normalized semantic feature vector associated with a search query, discovering the target asset based on the normalized semantic feature vector, generating a normalized latent feature vector associated with the target asset, comparing the normalized semantic feature vector with semantic feature vectors for each of the digital assets to generate a semantic comparison value, comparing the normalized target latent feature vector with latent feature vectors for each of the digital assets to generate a latent comparison value, blending the semantic comparison vector value with the latent feature comparison vector value to create a target comparison value for each of the digital assets, and reporting the digital assets having the highest target comparison values to the user or group of users.