Electronic book content representation method based on local reconfiguration model

Abstract

The invention puts forward an electronic book content representation method based on a local reconfiguration model. The method comprises the following steps that: A: tree structure expression: for each electronic book, dividing the electronic book into a plurality of pages, dividing each page into a plurality of paragraphs, and organizing each electronic book into a three-layer tree structure of "electronic book-page-paragraph"; B: node feature expression: constructing a vocabulary, calculating a word distribution vector, and using principal component analysis to carry out dimensionality reduction and compression on the word distribution vector of each level of nodes; C: local reconstruction model establishment: using the information of a child node to reconstruct the parent node information, i.e., establishing the local reconstruction model, solving the local reconstruction model, and obtaining a reconstruction coefficient vector; D: the uniform vector expression of the tree structure: according to the reconstruction coefficient vector obtained in C, carrying out information fusion on the node and the child node thereof, and updating the feature vector expression of the node; andE: carrying out electronic book retrieval and recommendation based on contents.

Description

technical field [0001] The invention belongs to the field of text mining systems, and in particular relates to an e-book content representation method based on a local reconstruction model, and the method uses an e-book as the most original input. Background technique [0002] In recent years, with the widespread use of mobile reading devices, the number of e-books is increasing, so it is of great significance to design effective e-book recommendation algorithms to provide accurate and effective recommendations for users. [0003] Regarding the recommendation of e-books, the existing technologies can be mainly divided into two categories: collaborative filtering recommendation and content-based recommendation. Collaborative filtering method relies heavily on user behavior, and its recommendation process depends on similar preferences among users, and requires a certain number of users in the system to score. If there are not enough user ratings, or some books are not purcha...

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Problems solved by technology

However, this type of method only relies on the word frequency statistics of words in the text, and ignores the spatial distribution information of words, making it difficult for this method to distinguish two books with similar word frequencies but different spatial distributions of words.

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