US 2022/0284499 A1 · FEATURE-LEVEL RECOMMENDATIONS FOR CONTENT ITEMS

Abstract

Techniques are provided for generating feature-level recommendations for content items. One method comprises obtaining feature values related to a content item; applying the feature values to a trained engagement prediction model that generates an influence score for each feature value, wherein the influence score for each feature value indicates an influence of each respective feature value on a performance indicator associated with the content item; generating a recommendation for improving the performance indicator using the influence score for each feature value; and initiating a modification of the content item using the recommendation. Features can be selected using an artificial intelligence technique that performs a sub-image analysis on historical content items to evaluate an area of influence for a region of the historical content items when a feature value of a feature is changed. An automated feature extraction process may extract feature values using machine learning model.

Claims25

Dependent claims

A method, comprising:

obtaining a plurality of feature values related to a content item, wherein each given one of the plurality of feature values corresponds to a respective one of a plurality of features;
applying the plurality of feature values to at least one trained engagement prediction model that generates an influence score for each of the plurality of feature values, wherein the influence score for each of the plurality of feature values indicates an influence of each respective feature value on at least one performance indicator associated with the content item;
generating one or more recommendations for improving the at least one performance indicator associated with the content item using the influence score for each of the plurality of feature values;
and
initiating at least one modification of the content item using at least one of the one or more recommendations;
wherein the method is performed by at least one processing device comprising a processor coupled to a memory.

3 more elements

A non-transitory processor-readable storage medium having stored therein program code of one or more software programs, wherein the program code when executed by at least one processing device causes the at least one processing device to perform the following steps:

obtaining a plurality of feature values related to a content item, wherein each given one of the plurality of feature values corresponds to a respective one of a plurality of features;
applying the plurality of feature values to at least one trained engagement prediction model that generates an influence score for each of the plurality of feature values, wherein the influence score for each of the plurality of feature values indicates an influence of each respective feature value on at least one performance indicator associated with the content item;
generating one or more recommendations for improving the at least one performance indicator associated with the content item using the influence score for each of the plurality of feature values;
and
initiating at least one modification of the content item using at least one of the one or more recommendations.

2 more elements

Dependencies are inferred from the claim text.

Feature-level recommendations for content items

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Feature-level recommendations for content items

Life of this patent

Abstract

Claims25