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To compare entities of differing types and structural components, theartificial neural network paradigm was used to cross-compare structuralcomponents between heterogeneous documents. Trainable weighted structuralcomponents were input into machine-learned activation functions of the neurons.The model was used for matching news articles and videos, where the inputs andactivation functions respectively consisted of term vectors and cosinesimilarity measures between the weighted structural components. The model wastested with different weights, achieving as high as 59.2% accuracy for matchingvideos to news articles. A mobile application user interface for recommendingrelated videos for news articles was developed to demonstrate consumer value,including its potential usefulness for cross-selling products from unrelatedcategories.

Comparing heterogeneous entities using artificial neural networks of  trainable weighted structural components and machine-learned activation  functions