Commodity recommendation method and system, equipment and storage medium

A product recommendation and product technology, applied in the field of data analysis, can solve the problems of imprecise recommendation accuracy and difficulty in meeting the actual needs of users, and achieve the effects of strong timeliness and improved accuracy

Pending Publication Date: 2020-12-25
尚娱软件(深圳)有限公司
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  • Abstract
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Problems solved by technology

[0006] The embodiment of the present application provides a product recommendation method, system, device and storage medium, aiming to solve the problem that the rec

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  • Commodity recommendation method and system, equipment and storage medium
  • Commodity recommendation method and system, equipment and storage medium
  • Commodity recommendation method and system, equipment and storage medium

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Embodiment Construction

[0049] In order to better understand the above-mentioned technical solutions, exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited by the embodiments set forth herein. Rather, these embodiments are provided for more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0050] The invention provides a commodity recommendation device. Such as figure 1 as shown, figure 1 It is a schematic structural diagram of the hardware operating environment involved in the solution of the embodiment of the present invention.

[0051] It should be noted, figure 1 That is, a structural schematic diagram of the hardware operating environment ...

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Abstract

The invention discloses a commodity recommendation method and system, equipment and a storage medium. The method comprises the steps of: obtaining latest behavior log data generated when a user browses or purchases a commodity; processing the latest behavior log data by adopting a preset preference model group to generate a commodity preference vector of the user; processing the latest behavior log data by adopting a preset entity relationship model to generate a commodity vector; and calculating a recommendation probability of the commodity according to the commodity preference vector and thecommodity vector, and recommending the commodity according to the recommendation probability. With the commodity recommendation method and system, equipment and storage medium of the invention adopted, the problem that a recommendation algorithm adopted by an existing shopping network to recommend commodities to users is inaccurate in recommendation precision and hard to meet actual requirementsof the users is solved, timeliness of recommending the commodities to the users by a recommendation system of the shopping network is higher, commodity recommendation precision is improved, behavior data of browsing and purchasing the commodities by the users can be integrated, and user experience is improved, and therefore, the relationship between commodities is evaluated.

Description

technical field [0001] The present invention relates to the technical field of data analysis, in particular to a product recommendation method, system, device and storage medium. Background technique [0002] Online shopping, also called shopping, is a manifestation of e-commerce, which means that users browse and search for relevant information in a virtual shopping environment in order to complete shopping or related tasks, so as to provide the necessary information for purchasing decisions. , and practice the decision-making and buying process. [0003] Usually, after a user browses or clicks on a product, the background of the website will automatically save the user's operation traces, and recommend related products to the user based on the user's historical operation traces. However, the recommendation algorithms used by current shopping networks to recommend products to users include traditional recommendation algorithms and general recommendation algorithms. [000...

Claims

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Application Information

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IPC IPC(8): G06Q30/06
CPCG06Q30/0625G06Q30/0631
Inventor 陈启晗张攀陈伦广林培圻陈伟健魏新宇
Owner 尚娱软件(深圳)有限公司
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