Advertisement placement method and system using semantic analysis
First Claim
1. A method for selectively relating a first dataset to a second dataset, the method comprising the steps of:
- accessing a semantic representation associated with the first dataset and a semantic representation associated with the second dataset, wherein at least one of the semantic representation associated with the first dataset and the semantic representation associated with the second dataset is a trainable semantic vector generated based on at least one data point included in a respective dataset and known relationships between predetermined data points and predetermined categories;
determining a similarity between the semantic representation associated with the first dataset and the semantic representation associated with the second dataset; and
selectively relating the first dataset to the second dataset based on a result of the determining step.
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Abstract
An advertisement placement method and system for relating an advertisement to a dataset based on a trainable semantic vector (TSV) associated with the dataset and respective semantic representations of the advertisements. The trainable semantic vector associated with the dataset is generated based on at least one data point included in the dataset and known relationships between predetermined data points and predetermined categories. A comparison process is performed to determine a similarity between the trainable semantic vector associated with the dataset and the semantic representation associated with each of the plurality of advertisements. The system selectively relates one or more of the advertisements with the dataset based on a result of the comparison process. The selected advertisement or advertisements may be displayed with the dataset.
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Citations
20 Claims
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1. A method for selectively relating a first dataset to a second dataset, the method comprising the steps of:
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accessing a semantic representation associated with the first dataset and a semantic representation associated with the second dataset, wherein at least one of the semantic representation associated with the first dataset and the semantic representation associated with the second dataset is a trainable semantic vector generated based on at least one data point included in a respective dataset and known relationships between predetermined data points and predetermined categories;
determining a similarity between the semantic representation associated with the first dataset and the semantic representation associated with the second dataset; and
selectively relating the first dataset to the second dataset based on a result of the determining step. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18)
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19. An advertisement placement system for relating one of a plurality of advertisements to a dataset, the system comprising:
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a data processor configured to process data; and
a data storage system configured to store instructions which, upon execution by the data processor, controls the data processor to perform the steps of;
accessing a semantic representation associated with a first dataset and a semantic representation associated with a second dataset, wherein at least one of the semantic representation associated with the first dataset and the semantic representation associated with the second dataset is a trainable semantic vector generated based on at least one data point included in a respective dataset and known relationships between predetermined data points and predetermined categories;
determining a similarity between the semantic representation associated with the first dataset and the semantic representation associated with the second dataset; and
selectively relating the first dataset to the second dataset based on a result of the determining step.
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20. A machine-readable medium carrying instructions which, upon execution of a data processing system, controls the data processing system to perform the machine-implemented steps of:
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accessing a semantic representation associated with a first dataset and a semantic representation associated with a second dataset, wherein at least one of the semantic representation associated with the first dataset and the semantic representation associated with the second dataset is a trainable semantic vector generated based on at least one data point included in a respective dataset and known relationships between predetermined data points and predetermined categories;
determining a similarity between the semantic representation associated with the first dataset and the semantic representation associated with the second dataset; and
selectively relating the first dataset to the second dataset based on a result of the determining step.
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Specification