Modeling interestingness with deep neural networks
First Claim
1. A computer-implemented process, comprising:
- applying a computer to perform process actions for;
receiving a collection of source and target document pairs;
identifying a separate context for each source document, the context for each source document comprising a selection within the source document and a window of multiple words in the source document around that selection;
identifying a separate context for each target document, the context for each target document comprising a first fixed number of the first words in that target document;
mapping each context to a separate vector;
mapping each of the vectors to a convolutional layer of a neural network;
mapping the convolutional layer to a plurality of hidden layers of the neural network;
generating a learned interestingness model by learning weights for each of a plurality of transitions between the layers of the neural network, such that the learned weights minimize a distance between the vectors of the contexts of the source and target documents;
the interestingness model configured to determine a conditional likelihood of a user interest in transitioning to an arbitrary target document when that user is consuming an arbitrary source document in view of a context extracted from that arbitrary source document and a context extracted from that arbitrary target document; and
applying the interestingness model to recommend one or more arbitrary target documents to the user relative to an arbitrary source document being consumed by the user.
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Accused Products
Abstract
An “Interestingness Modeler” uses deep neural networks to learn deep semantic models (DSM) of “interestingness.” The DSM, consisting of two branches of deep neural networks or their convolutional versions, identifies and predicts target documents that would interest users reading source documents. The learned model observes, identifies, and detects naturally occurring signals of interestingness in click transitions between source and target documents derived from web browser logs. Interestingness is modeled with deep neural networks that map source-target document pairs to feature vectors in a latent space, trained on document transitions in view of a “context” and optional “focus” of source and target documents. Network parameters are learned to minimize distances between source documents and their corresponding “interesting” targets in that space. The resulting interestingness model has applicable uses, including, but not limited to, contextual entity searches, automatic text highlighting, prefetching documents of likely interest, automated content recommendation, automated advertisement placement, etc.
90 Citations
19 Claims
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1. A computer-implemented process, comprising:
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applying a computer to perform process actions for; receiving a collection of source and target document pairs; identifying a separate context for each source document, the context for each source document comprising a selection within the source document and a window of multiple words in the source document around that selection; identifying a separate context for each target document, the context for each target document comprising a first fixed number of the first words in that target document; mapping each context to a separate vector; mapping each of the vectors to a convolutional layer of a neural network; mapping the convolutional layer to a plurality of hidden layers of the neural network; generating a learned interestingness model by learning weights for each of a plurality of transitions between the layers of the neural network, such that the learned weights minimize a distance between the vectors of the contexts of the source and target documents; the interestingness model configured to determine a conditional likelihood of a user interest in transitioning to an arbitrary target document when that user is consuming an arbitrary source document in view of a context extracted from that arbitrary source document and a context extracted from that arbitrary target document; and applying the interestingness model to recommend one or more arbitrary target documents to the user relative to an arbitrary source document being consumed by the user. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15)
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16. A system comprising:
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a general purpose computing device; and a computer program comprising program modules executable by the computing device, wherein the computing device is directed by the program modules of the computer program to; receive a collection of source and target document pairs; identify a separate focus and a separate context for each source document and each target document; the context of each source document comprising a selection of one or more words within the source document and a window of multiple words in the source document around that selection; the focus of each source document comprising a selected anchor within the source document; the context of each target document comprising a first fixed number of the first words in that target document; the focus of each target document comprising a second fixed number of the first words in that target document, the second fixed number being smaller than the first fixed number; map the words of each focus to a separate vector and the words of each context to a separate vector; for each document, concatenate the corresponding focus and context vectors into a combined vector; map each of the combined vectors to a convolutional layer of a neural network; map the convolutional layer to a hidden layer of the neural network; generate a learned interestingness model by learning weights for each of a plurality of transitions between the layers of the neural network, such that the learned weights minimize a distance between the combined vectors of the source and target documents; the interestingness model configured to determine a conditional likelihood of a user interest in transitioning to an arbitrary target document when that user is consuming an arbitrary source document in view of a context extracted from that arbitrary source document and a context extracted from that arbitrary target document; and applying the interestingness model to recommend one or more arbitrary target documents to the user relative to an arbitrary source document being consumed by the user. - View Dependent Claims (17, 18)
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19. A computer-readable storage device having computer executable instructions stored therein, said instructions causing a computing device to execute a method comprising:
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receiving a collection of source and target document pairs; identifying a separate context for each source document and each target document; the context for each source document comprising a selection within the source document and a window of multiple words in the source document around that selection; the context for each target document comprising a first fixed number of the first words in that target document; mapping each context to a separate vector; mapping each of the vectors to a convolutional layer of a neural network; mapping the convolutional layer to a plurality of hidden layers of the neural network; generating a learned interestingness model by learning weights for each of a plurality of transitions between the layers of the neural network, such that the learned weights minimize a distance between the vectors of the source and target documents; and training a discriminative model from an output layer of the learned interestingness model; and applying the discriminative model to automatically highlight content in an arbitrary document being consumed by that user.
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Specification