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Personalized recommendation algorithm combined with comment text mining

A recommendation algorithm and text mining technology, applied in unstructured text data retrieval, text database query, calculation, etc., can solve problems such as complex structure and inconspicuous effect

Pending Publication Date: 2020-11-13
GUANGXI TEACHERS EDUCATION UNIV
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Problems solved by technology

Among them, the recommendation algorithm using deep learning technology is better, but the structure is relatively complex and the effect is not obvious

Method used

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  • Personalized recommendation algorithm combined with comment text mining
  • Personalized recommendation algorithm combined with comment text mining
  • Personalized recommendation algorithm combined with comment text mining

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Embodiment Construction

[0054] The present invention will be further described in detail below in conjunction with the accompanying drawings, so that those skilled in the art can implement it with reference to the description.

[0055] A personalized recommendation algorithm combined with review text mining, including:

[0056] Use the traditional latent semantic model to obtain user features P u ;

[0057] Obtain item features Qi using stacked denoising autoencoders;

[0058] P u and item features Qi are fed into a multi-layer perceptron to predict ratings

[0059] Latent Factor Model (LFM) is an effective latent semantic analysis technology, which belongs to the model-based collaborative filtering algorithm, and is often used as a benchmark model for comparison of recommendation algorithms. Its core idea is to link users and items through latent features, and map user-item information into a joint latent semantic space with dimension F. The personalized recommendation algorithm combined with...

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Abstract

The invention discloses a personalized recommendation algorithm combined with comment text mining. The personalized recommendation algorithm comprises the steps: obtaining a user feature Pu by utilizing a traditional latent semantic model; obtaining a project feature Qi by using a stack noise reduction automatic encoder; inputting the user feature Pu and the project feature Qi into a multilayer perceptron to predict a score. According to the algorithm, a personalized recommendation algorithm is designed by utilizing the comment text; on the principle of improving recommendation accuracy, a comment text is modeled without a complex generation process, project features are obtained in combination with an existing mature stacked noise reduction automatic encoder (SDAE) architecture, and comment text data is used for recommendation. Compared with the existing algorithm, the algorithm has the characteristics of high recommendation precision, simple recommendation model and the like.

Description

technical field [0001] The invention belongs to the field of commodity personalized recommendation. More specifically, the present invention relates to a personalized recommendation algorithm combined with review text mining. Background technique [0002] The recommendation system has been highly concerned by various application fields because of its characteristics of solving "information overload" and providing personalized services. The core of the recommendation system is the recommendation algorithm. The existing recommendation algorithms can be mainly divided into three categories: content-based recommendation, collaborative filtering and hybrid recommendation, among which the most commonly used is the collaborative filtering algorithm. However, due to the sparseness of data, the recommendation effect of traditional collaborative filtering algorithms is not satisfactory. Therefore, researchers usually use metadata such as attributes and tags of users and items to mak...

Claims

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Application Information

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IPC IPC(8): G06F16/335G06F16/33G06F40/289G06F40/30G06N3/04G06N3/08
CPCG06F16/335G06F16/3344G06F40/289G06F40/30G06N3/084G06N3/045Y02D10/00
Inventor 陆建波刘春霞
Owner GUANGXI TEACHERS EDUCATION UNIV
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