User characteristic prediction method based on user purchase behavior

A technology of user characteristics and prediction methods, applied in neural learning methods, data processing applications, sales/rental transactions, etc., can solve problems such as loss of associated features, and achieve the effect of avoiding feature loss

Active Publication Date: 2021-03-12
HANGZHOU DIANZI UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

In addition, most of the above did not mine the association between users and items, and most of them regard user feature prediction as a classification task, and each feature of the user is relatively independent, resulting in a certain degree of loss of the associated features between users and items , cannot effectively learn a user representation vector for user feature prediction

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  • User characteristic prediction method based on user purchase behavior
  • User characteristic prediction method based on user purchase behavior
  • User characteristic prediction method based on user purchase behavior

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Embodiment Construction

[0021] The specific embodiments of the present invention will be further described in detail below in conjunction with the accompanying drawings. Its specific process is described as figure 1 shown, where:

[0022] Step 1: Collect target user characteristic information, historical order information, and order product information;

[0023] Target user characteristic information includes user portrait information, such as gender and age;

[0024] Historical order information includes the order user ID and purchase item ID;

[0025] The order product information includes the product name;

[0026] Step 2: Segment the product name in the product information of the order in step 1, extract the entities that contain certain information in the product name, and construct an entity set S(e) that contains all entities in the product name;

[0027] The entity may be a brand name, trade name, etc.;

[0028] Step 3: Build the Knowledge Subgraph

[0029] Find each entity e in the e...

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Abstract

The invention discloses a user characteristic prediction method based on a user purchase behavior, and the method comprises the steps: collecting target user characteristic information, historical order information and order commodity information, obtaining triple knowledge related to a commodity from a public knowledge graph through the order commodity information, constructing a knowledge sub-graph, aggregating entity neighbor local features by using a graph convolutional neural network, and fully learning representation vectors of entities. In the user feature prediction model, the similarity between the target user and different commodities and the similarity between the target user and users with similar purchase behaviors are learned according to different commodity features and different features of the users with similar purchase behaviors, feature vectors of the target user and the users with similar purchase behaviors are learned fully according to the similarity between theusers and the similarity between user entities, and personalized requirements of the users are met. According to the method, the accuracy of user feature prediction is improved, so that the user features can be predicted more accurately, and a more complete user portrait is constructed.

Description

technical field [0001] The present invention relates to a method for predicting user characteristics, more specifically, the present invention relates to a method for predicting user characteristics based on user historical purchase records. Background technique [0002] Nowadays, people are exposed to various online platforms in their daily lives. In addition to providing relevant services to users, these network platforms also leave "footprints" on these networks. These "footprints" are not only real and visible direct data such as users' personal attributes, published content, favorites, and purchases, but also There are also a large number of indirect data of users, such as click data, follow relationship and other behavior data. It is of great significance to the platform to construct accurate, comprehensive and effective virtual portraits of users on the Internet by using the data left by users in the network, and then promote the improvement of the performance of the...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F16/36G06F16/9535G06F40/289G06K9/62G06N3/04G06N3/08G06Q30/06
CPCG06F16/367G06F40/289G06F16/9535G06N3/084G06Q30/0631G06N3/045G06F18/2411
Inventor 周仁杰刘畅张纪林万健赵乃良胡强谢忠毅殷昱煜蒋从锋
Owner HANGZHOU DIANZI UNIV
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