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Attribute fusion interactive recommendation model construction method and system based on enhanced graph convolution

A construction method and convolution technology, applied in the field of information processing, can solve problems such as the inability to directly use the recommended model, achieve the effect of improving robustness and interpretability, and improving model performance

Pending Publication Date: 2022-06-28
QILU UNIV OF TECH
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Therefore, it cannot be directly used in the recommendation model

Method used

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  • Attribute fusion interactive recommendation model construction method and system based on enhanced graph convolution
  • Attribute fusion interactive recommendation model construction method and system based on enhanced graph convolution
  • Attribute fusion interactive recommendation model construction method and system based on enhanced graph convolution

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Embodiment 1

[0058] This embodiment discloses a method for constructing an attribute fusion interactive recommendation model based on enhanced graph convolution, and the attribute fusion interactive recommendation model based on enhanced graph convolution is named PGIR.

[0059] The source of this is explained below: the high-order synergy signal of user items is hidden in the user-item interaction. Below, we analyze the high-order synergy relationship of user items through specific examples. figure 1 is a bipartite graph derived from user-item interaction information (reviews), showing high-order connectivity among user-items. figure 1 The bipartite graph in (a) consists of three users (u1, u2, u3) and their interacting three items (v1, v2, v3), and each user-item pair has a time (t) at which the interaction occurs. figure 1 (b) represents two tree-like structures with user u1 as the node, representing the high-order connectivity of the user at t5 and t4, respectively. Obviously, user u...

Embodiment 2

[0143] The purpose of this embodiment is to provide a computing device, including a memory, a processor, and a computer program stored in the memory and running on the processor, and the processor implements the steps of the above method when the processor executes the program.

Embodiment 3

[0145] The purpose of this embodiment is to provide a computer-readable storage medium.

[0146] A computer-readable storage medium having a computer program stored thereon, the program executing the steps of the above method when executed by a processor.

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Abstract

The invention provides an attribute fusion interactive recommendation model construction method and system based on enhanced graph convolution, and the method comprises the steps: constructing a relation encoder based on enhanced graph convolution, extracting high-order features between user items from user item interaction behaviors, and obtaining dynamic feature representations, containing high-order cooperative signals, of users and items at all moments; and constructing a comment encoder fusing attribute information, and performing corresponding processing and fusion on the comment text and the corresponding attribute to obtain accurate feature representation of the comment. The analysis and research show that the attribute activation method provided by the PGIR can well solve the problem that the negative user recommendation is inaccurate, so that the model performance is improved, and the robustness and interpretability of the recommendation are improved at the same time.

Description

technical field [0001] The invention belongs to the technical field of information processing, and in particular relates to a method and system for constructing an attribute fusion interactive recommendation model based on enhanced graph convolution. Background technique [0002] The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art. [0003] The purpose of a recommender system is to recommend relevant items to users. So far, recommender systems have achieved great success in many applications. These applications collect a large number of user-item interaction records, which provide an unprecedented opportunity for the recommendation system to achieve accurate recommendation, thereby greatly reducing the complexity of user decision-making. Comments are the most common form of displayed feedback, and represent the most authentic user experience with an item. Some researches focus on...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F16/9535G06F16/9536G06F40/169G06Q30/06G06N3/04G06N3/08
CPCG06F16/9535G06F16/9536G06F40/169G06Q30/0629G06Q30/0631G06N3/08G06N3/048G06N3/045
Inventor 杨振宇王钰崔来平李怡雯
Owner QILU UNIV OF TECH
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