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AI algorithm fusion recommendation method and system

A recommendation method and algorithm technology, applied in computing, instrumentation, electrical digital data processing, etc., to achieve the effect of improving user experience

Inactive Publication Date: 2019-11-22
知鱼智联科技股份有限公司
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0003] To sum up, the existing mainstream algorithms have the following problems: when a large amount of user item interaction data cannot be obtained, how to realize the recommendation in the field of food, the accurate judgment of automatic intention in the context, and the recommendation of precise food stores

Method used

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  • AI algorithm fusion recommendation method and system
  • AI algorithm fusion recommendation method and system

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Embodiment 1

[0039] Such as Figure 1-Figure 2 as shown, figure 1 A schematic diagram of an AI algorithm fusion recommendation method provided by an embodiment of the present invention, including:

[0040] S101. Obtain historical user comment data according to the stores in advance, and perform word segmentation processing on the comment data to obtain word-segmented text.

[0041] It is understandable that on the platform or APP, users can comment after consumption in the store, and then the corresponding comment data can be obtained through the user's account. Those skilled in the art can understand that the user can post a comment only after logging in, so the user account corresponding to each comment can be obtained.

[0042] In a preferred embodiment of the present invention, the step of obtaining the comment data of historical users according to the store in advance, and performing word segmentation processing on the comment data, and obtaining the text after word segmentation inc...

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Abstract

The embodiment of the invention discloses an AI algorithm fusion recommendation method and system, and the method comprises the steps: obtaining comment data of a historical user in advance accordingto a store, carrying out word segmentation of the comment data, and obtaining a text after word segmentation; constructing a prediction model based on deep learning on the basis of texts and categories after word segmentation; according to the label and the historical record of the target user, adopting the prediction model to score the photograph probability of the target user for each type of store; and performing store recommendation based on the score. By applying the embodiment of the invention, automatic recommendation of the store can be realized, and the user experience is improved.

Description

technical field [0001] The invention relates to the field of data management of smart parks, and in particular to a recommendation method and system for AI algorithm fusion. Background technique [0002] In various recommendation fields, the more mature algorithms on the market include: user- or item-based collaborative filtering algorithms, user-item matrix decomposition / recommendation algorithms based on graph models or relational networks. These algorithms are powerful and useful when there is a large amount of data about user interactions with items. However, in many cases, we cannot obtain the interaction data between a large number of users and the items to be recommended, and it is likely that there are only unilateral data of users or items. If there is only one-sided data of users, in general, only one-sided feature attributes of users or items can be mined, which only serves as a model for rough classification of targets and matching with each other. However, the...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F16/9535G06F16/35G06F17/27G06Q30/06
CPCG06F16/9535G06F16/35G06Q30/0631
Inventor 刘小健潘鸿铮吴聿建
Owner 知鱼智联科技股份有限公司
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