Identifying categorized misplacement
First Claim
1. A method comprising:
- obtaining a word frequency of one or more words in a product title under a first category and another word frequency of the one or more words in the product title under a second category;
calculating a first overall word frequency of the product title under the first category based on the word frequency of the one or more words in the product title under the first category and a second overall word frequency of the product title under the second category based on the word frequency of the one or more words in the product title under the second category;
setting a first threshold for the first category and a second threshold for the second category;
storing the first threshold and the second threshold in a storage device; and
comparing the first overall word frequency of the product title with the first threshold and the second overall word frequency of the product title with the second threshold to determine a category of the product title.
1 Assignment
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Accused Products
Abstract
The present disclosure provides methods and devices for identifying category misplacement. In one embodiment, an example device obtains a word frequency of each respective word in a product title under a current category, calculates an overall word frequency of the product title under the current category based on the word frequency of each respective word under the current category, and compares the overall word frequency of the product title with a threshold of the current category to determine an existence of category misplacement. The techniques can accurately identify category misplacement and reduce the probability of missing identifying category misplacement. The techniques also require less system resources and improve calculation efficiency.
23 Citations
20 Claims
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1. A method comprising:
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obtaining a word frequency of one or more words in a product title under a first category and another word frequency of the one or more words in the product title under a second category; calculating a first overall word frequency of the product title under the first category based on the word frequency of the one or more words in the product title under the first category and a second overall word frequency of the product title under the second category based on the word frequency of the one or more words in the product title under the second category; setting a first threshold for the first category and a second threshold for the second category; storing the first threshold and the second threshold in a storage device; and comparing the first overall word frequency of the product title with the first threshold and the second overall word frequency of the product title with the second threshold to determine a category of the product title. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8)
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9. A method comprising:
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obtaining one or more recommended categories for each of multiple words in a product title; combining the one or more recommended categories for each of the multiple words to obtain a plurality of recommended categories for the product title; using a word frequency of a respective word in the multiple words under each of the plurality of recommended categories for the product title as a weight of the respective word under a respective recommended category for the product title; calculating a sum of weights of the multiple words for each of the plurality of recommended categories for the product title; and choosing one or more recommended categories for the product title based on a result of calculating the sum of weights of the multiple words for each of the plurality of recommended categories. - View Dependent Claims (10, 11, 12, 13, 14, 15, 16)
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17. A method comprising:
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obtaining one or more recommended categories for a product title; obtaining a product title vector and a respective category vector for a respective recommended category, one or more elements of the product title vector including a probability of appearance of one or more words in the product title, and one or more elements of the respective category vector including a word frequency of the one or more words under the respective recommended category; obtaining a respective product of the product title vector and the category vector for the respective recommended category; and choosing a target category from the one or more recommended categories based on a result of the respective product for the respective recommended category. - View Dependent Claims (18, 19, 20)
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Specification