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Personalized recommendation method and device, server and medium

A recommendation method and processor technology, applied in the computer field, can solve problems affecting the accuracy of personalized recommendations, and achieve the effects of improving recommendation accuracy, response rate, and conversion rate

Active Publication Date: 2019-01-11
BEIJING BAIDU NETCOM SCI & TECH CO LTD
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, since the user profile is obtained based on the user's historical network data, the obtained content of the user's interest is only the content of the user's previous interest, which affects the accuracy of personalized recommendation

Method used

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  • Personalized recommendation method and device, server and medium
  • Personalized recommendation method and device, server and medium
  • Personalized recommendation method and device, server and medium

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0025] figure 1 It is a flow chart of the personalized recommendation method provided by Embodiment 1 of the present invention. This embodiment is applicable to the situation of personalized recommendation for users. The method can be executed by a personalized recommendation device, which can use software and / or It is realized by hardware and can be integrated on the server. Such as figure 1 As shown, the method may include:

[0026] S110. According to the user profile, use the pre-trained demand intention model to predict the demand user group that has demand for the recommended object from the original user group.

[0027] Among them, the user portrait is a tagged embodiment of user information. Using the demand intention model, predict the demand user group for the recommended object from the original user group, that is, judge whether the user has the qualification to be recommended, and realize the preliminary screening of the user group.

[0028] Optionally, the use...

Embodiment 2

[0047] figure 2 It is a flow chart of the personalized recommendation method provided by Embodiment 2 of the present invention, and this embodiment is further optimized on the basis of the foregoing embodiments. Such as figure 2 As shown, the method may include:

[0048] S210. According to the user profile, use the pre-trained demand intention model to predict the demand user group that has demand for the recommended object from the original user group.

[0049] S220. Using the pre-trained intention recognition model and the user portrait of each user in the required user group, predict the behavior intention of each user, wherein the intention recognition model is a multi-classification model, and the predicted behavior intention of each user includes according to At least one intention in the order of the predicted score, the higher the predicted score, the stronger the intention.

[0050] That is, the output result of the intention recognition model includes the user's...

example 1

[0056] Example 1. The recommended object belongs to video. The attribute features of the video include the following five features: high-definition, fashion, makeup, teaching, and brand. The feature vectors corresponding to each attribute feature are: [1,0,0,0,0 ], [0,1,0,0,0], [0,0,1,0,0], [0,0,0,1,0], and [0,0,0,0,1], A non-zero number 1 indicates that the video under this type has attribute characteristics corresponding to the position of the element. Recommendable video 1 on the video website has attribute features of high-definition, fashion and makeup, then the video can be expressed as an attribute feature vector [1,1,1,0,0]; Recommendable video 2 on the video website has The attribute features are fashion, makeup and brand, then the video can be expressed as an attribute feature vector [0,1,1,0,1].

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Abstract

The embodiment of the invention discloses a personalized recommendation method and device, a server and a medium, wherein, the method comprises the following steps: according to a user portrait, usinga pre-trained demand intention model to predict a demand user group with a demand for a recommendation object from an original user group; utilizing the pre-trained intention recognition model and the user portrait of each user in the demand user group, the behavior intention of each user is predicted. The behavior intention of each user is matched with the attribute characteristics of the recommendation object, and the personalized recommendation is made for each user according to the matching result. The embodiment of the invention solves the problem of low accuracy of personalized recommendation for users, improves recommendation accuracy, further improves response rate of users after recommendation, and improves conversion rate of commodities or services.

Description

technical field [0001] The embodiments of the present invention relate to the field of computer technology, and in particular to a personalized recommendation method, device, server and medium. Background technique [0002] User portrait is a tagged user model abstracted based on information such as the user's social attributes, living habits, and consumption behavior. Through user portraits, user statistics and potential users can be analyzed to achieve precise marketing. Big data mining and analysis can also be performed based on user portraits to improve product operations and service quality. In the general environment of the Internet, personalized recommendation based on user portraits has been applied in many fields. [0003] In the prior art, it is usually to analyze the content that the user is interested in based on the user portrait, and then recommend related commodities or services to the user. However, since the user profile is obtained based on the user's his...

Claims

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Application Information

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IPC IPC(8): G06F16/9535
Inventor 刘昊骋王延熇田鹏飞
Owner BEIJING BAIDU NETCOM SCI & TECH CO LTD
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