Cold start recommendation model evaluation method and system, computer equipment and storage medium

An evaluation system and cold start technology, applied in the computer field, can solve the problem of insufficient positive and negative sample data index calculation, and achieve the effect of simple and easy-to-understand purposes and advantages.

Pending Publication Date: 2021-08-06
SHANGHAI MININGLAMP ARTIFICIAL INTELLIGENCE GRP CO LTD
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, in the case of cold start (that is, the initial stage of the recommendation model when the user has no click feedback behavior data), there is not enough positive and negative sample data for index calculation

Method used

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  • Cold start recommendation model evaluation method and system, computer equipment and storage medium
  • Cold start recommendation model evaluation method and system, computer equipment and storage medium
  • Cold start recommendation model evaluation method and system, computer equipment and storage medium

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Embodiment Construction

[0051]In order to make the purpose, technical solutions and advantages of the present application clearer, the present application will be described and illustrated below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described here are only used to explain the present application, and are not intended to limit the present application. Based on the embodiments provided in the present application, all other embodiments obtained by persons of ordinary skill in the art without creative efforts shall fall within the protection scope of the present application.

[0052] Obviously, the accompanying drawings in the following description are only some examples or embodiments of the present application, and those skilled in the art can also apply the present application to other similar scenarios. In addition, it can also be understood that although such development efforts may be complex and lengthy, for those of o...

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Abstract

The invention relates to a cold start recommendation model evaluation method and system, a computer and a readable storage medium, and the method comprises the steps: an evaluation sample obtaining step of selecting N target users from a target user group as samples; a material alternative set acquisition step of selecting M to-be-recommended materials associated with each target user in the to-be-evaluated recommendation model application scene; a material alternative set screening step of obtaining a preference score of each target user on the to-be-recommended material, normalizing the preference score, and screening the to-be-recommended material according to the preference score to obtain a screened material; a to-be-evaluated model scoring step of establishing positive and negative samples, scoring the positive and negative samples, and splicing the positive and negative samples into a multi-dimensional vector; and an evaluation index obtaining step of calculating the multi-dimensional vector by using the sorting evaluation index to obtain a corresponding evaluation index, and performing weighted statistics to obtain an evaluation index of the recommendation model. According to the method and the system, the performance of the recommendation model is accurately evaluated under the condition that the user feedback data is lacked.

Description

technical field [0001] The present application relates to the field of computer technology, in particular to a cold-start recommendation model evaluation method, system, computer equipment and computer-readable storage medium. Background technique [0002] With the growth of Internet services, users can obtain more online goods or content, and the data of interaction between these users and items has contributed to a new service, that is, personalized recommendation service. More and more Internet companies have developed recommendation services for users, such as recommending different types of new works based on the user's viewing history, or recommending answers that we may be interested in based on our browsing history or questioning history. It can be seen that such a Services already exist widely in our lives. [0003] In the initial stage of recommendation model creation, user behavior feedback data is scarce. In this case, the effect of many recommendation models ca...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F11/34
CPCG06F11/3452Y02P90/30
Inventor 陈嘉真徐凯波
Owner SHANGHAI MININGLAMP ARTIFICIAL INTELLIGENCE GRP CO LTD
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