Recommendation method, device and equipment and readable storage medium

A recommendation method and inventory technology, applied in the recommendation field, can solve problems such as high selection cost, wide recommendation results, difficulty in meeting users' real-time consumption needs, etc., and achieve the effect of reducing user decision-making time and accurate recommendation

Pending Publication Date: 2019-12-03
BEIJING SANKUAI ONLINE TECH CO LTD
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

In this process, because only historical data can be analyzed to predict the future, the recommendation results are relatively general, and the cost of user selection is high, which makes it difficult to meet the real-time consumption needs of users.

Method used

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  • Recommendation method, device and equipment and readable storage medium
  • Recommendation method, device and equipment and readable storage medium
  • Recommendation method, device and equipment and readable storage medium

Examples

Experimental program
Comparison scheme
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Embodiment 1

[0035] refer to figure 1 , which shows a flow chart of the steps of a recommended method embodiment of the present disclosure, which may specifically include the following steps:

[0036] Step 101, in response to the recommendation request initiated by the user, obtain the historical consumption commodities of the user;

[0037] In the embodiment of the present disclosure, when the user uses the client to consume, if the consumption is instant, such as takeaway, it may take a lot of time for the user to find his favorite food in a large number of merchants. Therefore, users often use the recommendation function of the client. When the user triggers the recommendation function of the client, in response to the user's recommendation request, the client obtains the user's historical consumption product information through the server or the consumption data stored locally on the client. .

[0038] Step 102, obtaining the real-time supply commodities of the user within the preset...

Embodiment 2

[0059] refer to figure 2 , which shows a flow chart of the steps of a recommended method embodiment of the present disclosure, which may specifically include the following steps:

[0060] Step 201, in response to the recommendation request initiated by the user, obtain the historical consumption commodities of the user;

[0061] This step is the same as step 101 and will not be described in detail here.

[0062] Preferably, the historical consumer goods include one or more of the names of the historical consumer goods, the prices of the historical consumer goods, the merchants of the historical consumer goods, and the ratings of the historical consumer goods.

[0063] Step 202, obtaining the real-time supply commodities of the user within the preset distance range;

[0064] This step is the same as step 102 and will not be described in detail here.

[0065] Step 203, obtaining the product name, product price, product merchant, and product rating of the real-time supply pro...

Embodiment 3

[0110] refer to image 3 , which shows a structural block diagram of a recommended device embodiment of the present disclosure, and may specifically include the following modules:

[0111] A historical consumption product acquisition module 301, configured to acquire the user's historical consumption product in response to a recommendation request initiated by the user;

[0112] A real-time supply product acquisition module 302, configured to obtain the real-time supply product of the user within a preset distance range;

[0113] The preset question and answer acquisition module 303 is used to extract the preset question and answer matching the historical consumption commodity and the real-time supply commodity from the preset question database;

[0114] A question-and-answer path generation module 304, configured to generate a question-and-answer path for the preset question-and-answer topic according to the product knowledge map and the preset question-and-answer topic;

...

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PUM

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Abstract

The embodiment of the invention provides a recommendation method, device and equipment and a readable storage medium, and the method comprises the steps: responding to a recommendation request initiated by a user, and obtaining a historical consumption commodity of the user; obtaining real-time supply commodities of the user within a preset distance range; extracting preset questions and answers matched with the historical consumption commodities and the real-time supply commodities from a preset question database; generating a question and answer path of a preset question and answer purpose according to the commodity knowledge graph and the preset question and answer questions; according to the question and answer path, proposing the preset question and answer questions to the user in a multi-round question and answer mode, and obtaining preset answers selected by the user according to the preset questions; obtaining real-time consumption preference commodity attributes of the user according to a preset answer; and extracting the commodity with the matched real-time consumption preference commodity attribute from the real-time supply commodities as a candidate commodity, and recommending the candidate commodity to the user. The problem that in the prior art, data recommendation based on historical data is poor in pertinence is solved.

Description

technical field [0001] The present disclosure relates to the technical field of recommendation, and in particular to a recommendation, a device, a device and a readable storage medium. Background technique [0002] With the widespread application of mobile terminals in people's lives, people are generally accustomed to using mobile applications to obtain life information. Among them, through offline mining of user historical behavior data, it is a more common way to understand user interests and recommend products that meet their needs. . [0003] In the existing technology, various related features are usually abstracted from the user's historical consumption records, and machine learning algorithms are used to predict the most likely future consumption products of the user and recommend them to the user. In this process, because only historical data can be analyzed to predict the future, the recommendation results are relatively general, and the cost of user selection is ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F16/9535G06Q30/06
CPCG06F16/9535G06Q30/0631
Inventor 罗震
Owner BEIJING SANKUAI ONLINE TECH CO LTD
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