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- W2784011873 abstract "To compare entities of differing types and structural components, the artificial neural network paradigm was used to cross-compare structural components between heterogeneous documents. Trainable weighted structural components were input into machine-learned activation functions of the neurons. The model was used for matching news articles and videos, where the inputs and activation functions respectively consisted of term vectors and cosine similarity measures between the weighted structural components. The model was tested with different weights, achieving as high as 59.2% accuracy for matching videos to news articles. A mobile application user interface for recommending related videos for news articles was developed to demonstrate consumer value, including its potential usefulness for cross-selling products from unrelated categories." @default.
- W2784011873 created "2018-01-26" @default.
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- W2784011873 date "2018-01-09" @default.
- W2784011873 modified "2023-09-26" @default.
- W2784011873 title "Comparing heterogeneous entities using artificial neural networks of trainable weighted structural components and machine-learned activation functions." @default.
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