Method, device and server for obtaining similarity of key words

Abstract

The invention discloses a method, device and server for obtaining the similarity of key words, and belongs to the field of information technology. The method comprises the steps: obtaining key words of user labels and key words of interested classes; according to the key words of the user labels and the key words of the interested classes, looking for a preset database to obtain the word vector of each key word in the key words of the user labels and the word vector of each key word in the key words of the interested classes; computing a distance between the word vector of each key word in the key words of the user labels and the word vector of each key word in the key words of the interested classes according to the word vector of each key word in the key words of the user labels and the word vector of each key word in the key words of the interested classes; obtaining the distance between the word vector of a first key word and the word vector of a second key word to be used as the similarity of the first key word and the second key word. According to the invention, the word vectors are used for obtaining the similarity of the key words, so that the precision rate of recommended information is increased.

Description

technical field [0001] The present invention relates to the field of information technology, in particular to a method, device and server for acquiring keyword similarity. Background technique [0002] With the continuous development of information technology, how to recommend information to users who are interested in the information is an urgent problem to be solved. When recommending information to users, it is generally necessary to obtain the similarity between the keywords in the user's label and the keywords in the user's interest category, so that based on the similarity, the interest value of the keyword in the interest category is obtained, according to The size of the interest value recommends information to the user. [0003] In the process of obtaining similarity, you can use the full text of Soso Encyclopedia and Q&A content as a corpus, use each entry in the full text of Encyclopedia or the content of Q&A in Q&A as a document, and count the keywords in user t...

AI Technical Summary

Problems solved by technology

[0005] Since the similarity between any two keywords depends entirely on their document frequency in the corpus, and the proportion of the number of documents in which keywords appear in the corpus to the number of all documents in the corpus is extremely uneven, for example, two keywords may be very seldom appear in a document, but the frequency of two keywords appearing in the document is very high, so that the obtained similarity cannot accurately describe the connection between the two keywords, so that information is recommended to users in the follow-up low accuracy and success

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