Search keyword frequency analysis method, device, electronic device and medium

Abstract

The invention provides a method and device of analyzing search keyword frequency based on HLSA. In the method, keyword aggregation is conducted by introducing the LSA space model which contains a theme, the deficiency that the Euclidean distance model based on VSM vector does not take into account the semantic information of a word per se is overcome and the error caused by the order changes of keywords based on an edit distance model is remedied. Additionally the method further combines with Hamming keywords to make computations on the similarity of eigenvectors between the keywords, new HLSA algorithm is formed, the computation efficiency of similarity is increased; the K-nearest neighbor algorithm is utilized to classify and statistically measure the frequency of keywords, aggregation is conducted on keywords of different granularities, and misjudgments due to too small particle size by the longest common substring model are effectively avoided.

Description

technical field [0001] The invention relates to the technical field of electronic commerce, in particular to an HLSA-based search keyword frequency analysis method and device. Background technique [0002] The keywords entered by the user in the search bar of the e-commerce platform are important reference information to express their willingness to purchase a certain product. Aggregating, classifying, and counting the frequency of product search keywords within a predefined time period can effectively quantify the degree of user demand for a certain product, and then provide sales personnel with information on whether a certain product needs to be purchased, put on the shelf or enhanced Its promotion efforts provide a strong reference decision-making basis. [0003] The premise of counting the frequency of search keywords is to classify them, and the basis of classification theory almost always depends on the similarity model. At present, the methods for calculating the s...

AI Technical Summary

Problems solved by technology

Although the Levenshtein distance model has the advantage of not needing to consider whether the keywords are linearly independent, its disadvantage is that if the order of keywords changes, the similarity calculation results will have a large error

However, the longest common subsequence method can still work well in the case of discontinuous keywords, but because the basic feature granularity of its discriminative keywords is too fine, it often produces over-judgment, resulting in the introduction and accumulation of errors, so the use of occasions are often restricted

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