Book similarity calculation method based on random walk and electronic equipment

A random walk and similarity matrix technology, applied in the field of data processing, can solve problems such as poor accuracy, low book adoption rate, and inability to reflect the similarity of books from the user's perspective, so as to optimize the calculation method and improve calculation accuracy Effect

Pending Publication Date: 2019-12-03
ZHANGYUE TECH CO LTD
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, the above-mentioned book similarity calculation method is based on text content, which cannot reflect the similarity between books from the user's point of view, and the accuracy is poor, which leads to the recommendation when using the similarity between books to recommend books. Books have low adoption and poor recommendations

Method used

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  • Book similarity calculation method based on random walk and electronic equipment
  • Book similarity calculation method based on random walk and electronic equipment
  • Book similarity calculation method based on random walk and electronic equipment

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0031] figure 1 It shows a schematic flowchart of a random walk-based book similarity calculation method according to Embodiment 1 of the present invention, as shown in figure 1 As shown, the method includes the following steps:

[0032] Step S101, acquiring user interaction behavior data for books.

[0033] The user interaction behavior data for books is data used to describe the interaction between the user and the book, and may specifically include: book reading data, book review data, book download data, etc. for the book. User interaction behavior data implies the law of data change, which can be used to analyze the relationship between books. In step S101, user interaction behavior data such as book reading data, book review data, and book download data stored on the book reading platform may be obtained.

[0034] Step S102, according to the user interaction behavior data, determine the sequence of interactive books corresponding to each user.

[0035] Through the da...

Embodiment 2

[0044] figure 2 It shows a schematic flowchart of a random walk-based book similarity calculation method according to Embodiment 2 of the present invention, as shown in figure 2 As shown, the method includes the following steps:

[0045] Step S201, acquiring user interaction behavior data for books.

[0046]Among them, user interaction behavior data such as book reading data, book review data, and book download data of each user for books can be obtained from the book reading platform. The user interaction behavior data at least includes: user ID, books interacted by the user, interaction start time, interaction end time and other data. Specifically, the user ID may be the user's account on the book reading platform, such as a mobile phone number, user name, email address, WeChat ID, QQ number, and the like.

[0047] Step S202, for each user, perform data analysis on the user interaction behavior data corresponding to the user, and determine the books that the user has in...

Embodiment 3

[0086] Embodiment 3 of the present invention provides a non-volatile storage medium, where at least one executable instruction is stored in the storage medium, and the executable instruction can execute the random walk-based book similarity calculation method in any method embodiment above.

[0087] Specifically, the executable instructions can be used to make the processor perform the following operations: acquire user interaction behavior data for books; determine the interactive book sequence corresponding to each user according to the user interaction behavior data; construct the interactive book sequence corresponding to each user to obtain Book association graph: Perform random walk calculations based on the book association graph to obtain the book similarity matrix of each book relative to other books.

[0088] In an optional implementation manner, the executable instructions further cause the processor to perform the following operations: for each user, perform data an...

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PUM

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Abstract

The invention discloses a book similarity calculation method based on random walk, electronic equipment and a storage medium, and the method comprises the steps: obtaining user interaction behavior data for a book; determining an interactive book sequence corresponding to each user according to the user interaction behavior data; constructing a book association graph according to the interactive book sequence corresponding to each user; performing random walk calculation according to the book association graph to obtain a book similarity matrix of each book relative to other books. According to user interaction behavior data for books, the book association graph can be conveniently and rapidly constructed. Random walk calculation is made according to the book association graph, the book similarity matrix of each book relative to other books is rapidly obtained, the similarity between the books is accurately and effectively reflected from the perspective of a user, the calculation accuracy of the book similarity is effectively improved, and the calculation mode of the book similarity is optimized.

Description

technical field [0001] The invention relates to the technical field of data processing, in particular to a random walk-based book similarity calculation method, electronic equipment and a storage medium. Background technique [0002] Books in the form of e-books are favored by a large number of users due to their advantages such as easy access. Book reading platforms mostly recommend books according to the similarity of book content. Most of the calculation methods of book similarity in the prior art are to perform text recognition and analysis on the content of books, and obtain the similarity between books based on the analysis results. However, the above calculation method of book similarity is based on the text content, which cannot reflect the similarity between books from the user's point of view, and the accuracy is poor, which leads to the recommendation when using the similarity between books to recommend books. Books have low adoption rates and poor recommendatio...

Claims

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Application Information

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IPC IPC(8): G06F17/22
Inventor 王海璐曹雯潇
Owner ZHANGYUE TECH CO LTD
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