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Text recommendation method

A recommendation method and text technology, applied in special data processing applications, instruments, electronic digital data processing, etc., can solve the problems of not considering synonym matching, incomplete coverage of recommendation results, and low accuracy, so as to improve coverage and accuracy. rate, and the effect of good practical value

Inactive Publication Date: 2014-12-24
UNIV OF ELECTRONIC SCI & TECH OF CHINA
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Problems solved by technology

However, due to the flexibility of natural language, different documents may use synonyms with the same meaning when describing the same thing. If only word form matching is considered in the text recommendation process, and synonyms are not considered, the coverage of recommendation results will inevitably be incomplete and accurate. low rate problem

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Embodiment Construction

[0024] The content of the present invention will be further described below in conjunction with specific embodiments.

[0025] The processing flow of the existing technology is as follows: figure 1 As shown, in text recommendation, unstructured text information cannot be directly used in the recommendation algorithm, and the feature words and corresponding weights that best represent the text information in the text information need to be extracted through text preprocessing. Preprocessing text information includes establishing a vector space model, extracting feature words, and removing stop words.

[0026] The vector space model proposed by Salton et al. is a simple and efficient document representation model. The idea of ​​this model is that for any document set, based on its different feature words, a document feature word vector space is formed, and the document is represented by feature vectors in the space. There are usually two types of vector space models, namely th...

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Abstract

The invention discloses a text recommendation method. The text recommendation method comprises the following steps of establishing a user model; preprocessing a text document; extracting feature vectors; calculating the optimal matching of synonyms among the feature vectors; determining a recommended text according to the similarity between the feature vectors and the user model. According to the method, a matching factor for semantic similarity among the synonyms is added on the base of the traditional angle cosine algorithm, the influence of the synonyms of the text on the similarity is considered, the levels of similarity between texts and between the text and the user model are more accurately calculated, and through experimental verification, the text recommendation method has the advantage of improving the accuracy rate by 20 percent averagely compared with the angle cosine algorithm and has high practical value.

Description

technical field [0001] The invention belongs to the field of text classification, and in particular relates to a text recommendation method. Background technique [0002] With the advent of the information age, in order to find the required information from the massive texts, a lot of reading is required. The traditional method is to manually classify texts, organize and organize them, and provide people with a relatively effective means of information acquisition. However, there are many disadvantages in this traditional text classification method: first, it consumes a lot of manpower, material resources and energy; second, the text classification method cannot meet the needs of all users. This requires us to explore effective ways to personalize text services and improve text reading efficiency. In this context, an algorithm to further filter documents according to the user interest model - text recommendation algorithm came into being. [0003] General text recommendat...

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Application Information

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IPC IPC(8): G06F17/30G06F17/27
CPCG06F16/9535G06F40/30
Inventor 于富财伍盛李林胡光岷
Owner UNIV OF ELECTRONIC SCI & TECH OF CHINA
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