Personalized recommendation method based on a cyclic generative adversarial network

A recommendation method and network technology, applied in the computer field, can solve problems in the field of recommendation that have not yet been applied, and achieve the effect of avoiding the artificial feature extraction process

Active Publication Date: 2019-04-19
HANGZHOU NORMAL UNIVERSITY
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Generative confrontation networks have been used a lot in the field of image generation, but they have not been applied to the field of recommendation, especially in movie recommendation

Method used

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  • Personalized recommendation method based on a cyclic generative adversarial network
  • Personalized recommendation method based on a cyclic generative adversarial network
  • Personalized recommendation method based on a cyclic generative adversarial network

Examples

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Embodiment Construction

[0055] A specific implementation case of the present invention is further described below in conjunction with the accompanying drawings, as figure 1 Shown:

[0056] The data used in this example is MovieLens, which was founded by the GroupLens project team of the School of Computer Science and Engineering at the University of Minnesta in the United States. This dataset describes unlimited tags of movies within 5 stars for user recommendations. The dataset contains 100,004 ratings for 9,066 movies by 671 users. Users are randomly selected, and each selected user rates at least 20 movies.

[0057] Step (1). Construct matrix image:

[0058] Sampling the time series of the user's historical behavior with the method of equidistant sliding window and constructing matrix image sample set R:

[0059]

[0060] t is the total number of samples, n is the number of users, m is the number of items;

[0061] Taking 30 days as a cycle, a two-dimensional matrix image is generated ever...

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Abstract

The invention relates to a personalized recommendation method based on a cyclic generative adversarial network. According to the method, through sampling the historical behavior of the user based on the time information, a matrix sample fused with the time information is constructed, and automatic feature extraction and dynamic time sequence behavior display are realized by using a cyclic generative adversarial network. According to the method, firstly, a matrix image is constructed, a cyclic generative adversarial network is constructed, then the internal process of the cyclic generative adversarial network is cycled, the cyclic generative adversarial network is trained, finally, row pixel points of a certain user in the image are sequenced, and first N rows with large numerical values are listed as a recommendation list of the user. According to the method, the time information can be transmitted from the circulation of the generated information, the time is sliced, each model is anindependent network connected through information transmission, and the time sequence mode can be better reflected in the aspects of the model principle and the actual effect.

Description

technical field [0001] The invention belongs to the field of computer technology, and relates to a personalized recommendation method based on a cyclic generative confrontation network to predict the user's preference degree for item items. Background technique [0002] Personalization is one of the areas receiving the most attention right now. Today, with the rapid development of science and technology, more and more products make the market oversupply, and consumers need to choose their favorite products from many similar products. But now people pay more attention to the internal quality and price of products, and pay more attention to the diversification of material product forms, internalization and popularization of quality. With the increase of people's income, the shortening of popular consumption period and product life cycle, the imitation speed of fashionable consumption methods is accelerated, and the consumption concept is gradually developing towards the exter...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F16/9535G06Q30/06
CPCG06Q30/0631
Inventor 张子柯王睿修李丽霞刘闯
Owner HANGZHOU NORMAL UNIVERSITY
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