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Social group recommendation method, system, device and storage medium

A recommendation method and group technology, applied in the field of equipment and storage media, systems, and social group recommendation methods, can solve problems such as extreme value influence of member distribution, decline in recommendation accuracy, overfitting, etc., to enhance accuracy and communication capabilities , Improving the ability of dissemination and improving the effect of accuracy

Active Publication Date: 2022-07-15
UNIV OF SCI & TECH OF CHINA
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

The traditional heuristic aggregation strategy is affected by the extreme value of the distribution of member preferences, and cannot accurately model the group
[0004] With the development of deep learning technology, researchers propose to use representation learning technology to learn complex group preference feature representation, and use attention mechanism to learn the weight of each user in group decision-making, but these methods do not take into account most groups Due to the sparsity of historical interaction data, using insufficient data for training will cause serious overfitting problems and lead to a decrease in recommendation accuracy

Method used

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  • Social group recommendation method, system, device and storage medium
  • Social group recommendation method, system, device and storage medium
  • Social group recommendation method, system, device and storage medium

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Experimental program
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Embodiment 1

[0036] The embodiment of the present invention provides a social group recommendation method, such as figure 2 As shown, it mainly includes the following steps:

[0037] Step 1. Build a social group recommendation model and obtain social network data.

[0038] In the embodiment of the present invention, the social network data mainly includes the following three types of data: group-item interaction data, user-item interaction data within the group, and social network graphs between users within the group; the above three types of data are all conventional data, Users can collect through the Internet.

[0039] Step 2: Train the social group recommendation model.

[0040] In the training phase, the social network data is input into the social group recommendation model, and three types of information are extracted by the social group recommendation model:

[0041] 1) Use the relationship between nodes in the social network graph to calculate the social influence representat...

Embodiment 2

[0125] The present invention also provides a social group recommendation system, which is mainly implemented based on the method provided in the first embodiment, such as Figure 5 As shown, the system mainly includes:

[0126] The model building unit is used to build a social group recommendation model,

[0127] The data information acquisition unit is used to acquire the interaction data between the group and the item, the interaction data between the user and the item in the group, and the social network graph between the users in the group;

[0128] The training unit is applied in the training phase; in the training phase, the social group recommendation model uses the relationship between the nodes in the social network graph to calculate the social influence representation vector of the user corresponding to the node; based on the interaction data between users and items in the group and the user The social influence representation vector is calculated using the influen...

Embodiment 3

[0133] The present invention also provides a processing device, such as Image 6 As shown, it mainly includes: one or more processors; a memory for storing one or more programs; wherein, when the one or more programs are executed by the one or more processors, the One or more processors implement the methods provided by the foregoing embodiments.

[0134] Further, the processing device further includes at least one input device and at least one output device; in the processing device, the processor, the memory, the input device, and the output device are connected through a bus.

[0135] In this embodiment of the present invention, the specific types of the memory, input device, and output device are not limited; for example:

[0136] The input device can be a touch screen, an image capture device, a physical button or a mouse, etc.;

[0137] The output device can be a display terminal;

[0138] The memory may be random access memory (Random Access Memory, RAM), or may be non...

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Abstract

The invention discloses a social group recommendation method, system, equipment and storage medium, which can automatically calculate the user's social influence directly from the user's social network, and use the user's social influence to enhance the accuracy and dissemination ability of group recommendation; and The user influence diffusion method can be used to obtain deeper feature representations of users, and at the same time, the attention mechanism is used to learn the influence of each user in the decision-making process when modeling group preferences, so as to obtain group feature representations more effectively; Finally, through joint learning, both single-user recommendation and group user recommendation tasks are optimized to improve the performance of social group recommendation model, improve the accuracy of group recommendation, and improve the dissemination ability of group recommendation.

Description

technical field [0001] The present invention relates to the technical field of item recommendation, and in particular, to a social group recommendation method, system, device and storage medium. Background technique [0002] The advent of the Internet era and the rise of social networks have enabled the unhindered dissemination and exchange of information. According to statistics, in 2020, the number of netizens in the world will reach 4.54 billion, and the number of netizens in my country will reach 904 million. The explosive growth of information in social networks has brought about a serious problem of information overload. Recommendation algorithms can recommend content of interest to users according to their preferences. A large number of groups exist in social networks, in which users gather together through the same interests or specific events. Most of the existing studies focus on individual recommendations while ignoring group recommendations. like figure 1 As s...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06F16/9536G06Q50/00
CPCG06F16/9536G06Q50/01
Inventor 毛震东张勇东胡博白嘉萌
Owner UNIV OF SCI & TECH OF CHINA