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Group division method and system based on long-term and short-term memory network

A long-short-term memory and group technology, applied in neural learning methods, biological neural network models, neural architectures, etc., can solve the problems of being included in the calculation range and not mining, and achieve the effect of optimizing the recommendation results

Active Publication Date: 2021-06-18
LIAONING UNIVERSITY OF TECHNOLOGY
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  • Description
  • Claims
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AI Technical Summary

Problems solved by technology

[0005] The above group division method aggregates similar members to form a group to a certain extent, which effectively improves the accuracy of group recommendation results, but does not mine and include the influencing factors of user tendency in the acquisition of user tendency , there are certain defects

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  • Group division method and system based on long-term and short-term memory network

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Embodiment Construction

[0023] In the following description, for purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of one or more embodiments. It may be evident, however, that these embodiments may be practiced without these specific details. In other instances, well-known structures and devices are shown in block diagram form in order to facilitate describing one or more embodiments.

[0024] In order to better illustrate the technical solution of the present invention, some basic theories involved in the present invention will be briefly described below.

[0025] Long Short-Term Memory Network (LSTM, Long Short-Term Memory) is a time recurrent neural network that is good at processing time series data. Compared with the traditional recurrent neural network RNN, LSTM is able to deal with long-term dependency problems due to the introduction of a memory unit that can decide which states should be kept and which states should be forgotten. ...

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Abstract

The invention provides a group division method and system based on a long and short term memory network, and the method comprises the steps: carrying out the time sequence modeling according to the historical behavior information of a user, so as to form a time sequence data sequence; performing feature extraction on the time sequence data sequence through a preset long-short-term memory network model to obtain behavior feature information of the user migrated along with time; determining the implicit similarity of the users according to the behavior feature information of the users migrated along with the time; summing the user similarities according to the user explicit similarity and the user implicit similarity, and dividing the members with high similarity in the same group. The implicit tendency of the users is obtained through the recurrent neural network, the users are grouped according to the overall tendency of the users, and the problem of inaccurate user grouping caused by the dynamic tendency of the users is solved.

Description

technical field [0001] The present invention relates to the technical field of group recommendation, and more specifically, to a group division method and system based on a long short-term memory network. Background technique [0002] Group recommendation is a service form that meets the individual and common needs of group users. As an effective means to solve the recommendation problem, it has received more and more attention in the academic field. [0003] Group division is the first step in group recommendation, and its results have an important impact on subsequent preference fusion and prediction recommendations. Traditional group division techniques are divided into three types: random grouping, similarity calculation, and clustering. With the enrichment of data-related attributes and the requirement for more accurate recommendation results, more and more factors need to be considered for group division. Gradually shift from the traditional group size, group cohesion...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06N3/04G06N3/08
CPCG06N3/049G06N3/08G06N3/044
Inventor 梅红岩许晓明刘鑫李凯
Owner LIAONING UNIVERSITY OF TECHNOLOGY
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