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Deep learning recommendation method fusing score matrix and comment text

A deep learning and scoring matrix technology, applied in natural language data processing, special data processing applications, instruments, etc., can solve problems such as projects that are not too relevant

Inactive Publication Date: 2021-02-05
BEIJING UNIV OF TECH
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0012] Comment text contains a lot of useful information, some comment information can accurately describe the relevant characteristics of the project, while some comments have little relevance to the project

Method used

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  • Deep learning recommendation method fusing score matrix and comment text
  • Deep learning recommendation method fusing score matrix and comment text
  • Deep learning recommendation method fusing score matrix and comment text

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Embodiment Construction

[0035] Most of the existing models that fuse rating data and review texts use matrix decomposition algorithms when processing rating data, and only use the potential features in the rating data. In order to make better use of rating information, the present invention uses deep neural networks to process ratings The deep features in the data extraction. The convolutional neural network is used to process the comment text, and the attention mechanism is introduced to extract representative comments in the comment information, so as to better represent user preferences and item features. The features are fused to get the final prediction score.

[0036] The present invention comprises following specific steps:

[0037] First, preprocess the comment text in the dataset, the main work is as follows:

[0038] Load the data, perform standardization, and convert all letters in the comment information to lowercase. It then filters the comment text for punctuation and special symbols...

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Abstract

The invention discloses a deep learning method fusing scoring data and comment texts. The influence of scoring data sparsity is relieved by introducing auxiliary information. The preference information and the item characteristics of the user can be obtained by utilizing the comment text, and the score data also contains potential association between the user and the item. Most of existing fusionmodels adopt a matrix decomposition method to process score data, in order to better utilize effective information in the score data, a convolutional neural network is utilized to process comment texts, an attention mechanism is introduced to extract representative comments in comment information, and therefore, the score data fusion accuracy is improved. Therefore, user preferences and project characteristics can be better represented. By processing the score data by using a deep neural network to extract deep features in the score data and fusing the features, the method can predict the score of the project by the user.

Description

technical field [0001] The invention relates to a recommendation model that uses deep learning to extract text features and fuse scoring data, and belongs to the field of information recommendation. Background technique [0002] With the rapid development of the network, the amount of data increases exponentially, and it becomes more and more difficult for people to obtain useful information from massive amounts of data. Recommender systems have been proven to be effective in solving information overload, and can help people quickly find the information they need in massive amounts of data. [0003] Traditional recommendation algorithms mainly include collaborative filtering and content-based recommendation algorithms. These two methods are based on the user's historical behavior to make recommendations. The content-based recommendation algorithm uses the user's rating information on the item to determine the common characteristics between these items. If the new item also...

Claims

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Application Information

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IPC IPC(8): G06F16/9535G06F40/117G06F40/166G06F40/189G06F40/284G06N3/04
CPCG06F16/9535G06F40/284G06F40/117G06F40/189G06F40/166G06N3/047G06N3/045
Inventor 杜永萍王艳彭治
Owner BEIJING UNIV OF TECH
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