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A hybrid recommendation method for online shopping mall system

A hybrid recommendation and recommendation algorithm technology, applied in the field of recommendation, can solve problems such as sparse commodity rating matrix and inaccurate merchant recommendation list, and achieve the effect of increasing the number of common scores, shortening the time of interest prediction and evaluation, and making recommendations accurate

Active Publication Date: 2021-08-17
上海百秋新网商数字科技有限公司
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  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

In the field of product recommendation, due to the huge number of products on the website, but only a few users are willing to rate the products they have visited, the user-online product rating matrix is ​​extremely sparse, resulting in the merchant recommendation based on the collaborative filtering algorithm. List is not accurate enough

Method used

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  • A hybrid recommendation method for online shopping mall system
  • A hybrid recommendation method for online shopping mall system
  • A hybrid recommendation method for online shopping mall system

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Embodiment Construction

[0019] The technical solutions in the embodiments of the present invention will be described clearly and in detail below with reference to the drawings in the embodiments of the present invention. The described embodiments are only some of the embodiments of the invention.

[0020] The technical scheme that the present invention solves the problems of the technologies described above is:

[0021] Such as figure 1 Shown is a hybrid recommendation method for an online shopping mall system, which includes the following steps:

[0022] The user inputs the purchase request information, and obtains the user's historical purchase information, screens the user's historical purchase information, obtains information related to the online shopping mall and the user, obtains the user-online product rating matrix, and obtains the user-online product rating matrix from the preset user attribute items Determine the target attribute item associated with the purchase request information in t...

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Abstract

The present invention claims protection for a mixed recommendation method for an online shopping mall system, which includes the following steps: the user inputs purchase request information, and obtains the user's historical purchase information, screens the user's historical purchase information, and acquires the information related to the online shopping mall. and user-related information, get the user-online product rating matrix, construct a historical scoring record set, construct the final similar product set, use the similar product set of the target product to fill the user-online product rating matrix; and use similar online products to Fill in the user-online product rating matrix again to make preliminary recommendations for online products; divide the user's historical purchase data into single-dimensional user feature values ​​and multi-dimensional user feature values ​​to determine the recommendation list. The hybrid recommendation method of the present invention can improve the accuracy of commodity recommendation.

Description

technical field [0001] The invention belongs to the technical field of recommendation, in particular to a mixed recommendation method for an online shopping mall system. Background technique [0002] The existing product recommendation in the mall is not refined enough, mainly based on the number of clicks. If a user clicks on a certain product more times, when the user opens the webpage again, the display order of the product will be at the top. However, the analysis of this recommendation method There are few dimensions to consider, and it cannot truly reflect the products that users actually want. It is not systematic enough, and the products actually recommended may not be what users really want. [0003] Therefore, how to realize that the order of commodities recommended by the online shopping mall is more in line with the needs of users has become an urgent problem to be solved. The most common is the collaborative filtering algorithm. In the field of product recomme...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06Q30/06
CPCG06Q30/0625G06Q30/0631
Inventor 成英
Owner 上海百秋新网商数字科技有限公司
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