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User behavior prediction and corresponding information recommendation method and device

A prediction method and user technology, applied in the information field, can solve problems such as time-consuming machine resources, impossibility, insufficient data, etc., and achieve the effect of simple user behavior prediction and flexible user behavior prediction

Active Publication Date: 2021-12-24
ALIBABA (CHINA) CO LTD
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AI Technical Summary

Problems solved by technology

The above model training takes a lot of time and machine resources, and cannot be done in the case of insufficient data

Method used

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  • User behavior prediction and corresponding information recommendation method and device
  • User behavior prediction and corresponding information recommendation method and device
  • User behavior prediction and corresponding information recommendation method and device

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Embodiment Construction

[0028] Preferred embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although preferred 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 to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.

[0029] As mentioned in the background technology section, in information applications, such as news and content aggregation APPs, it is often necessary to make personalized recommendations to users based on their behavior attribute information, so as to recommend information that matches their browsing behaviors to users, and improve user experience. satisfaction. At present, when using algorithms such as collaborative filtering, matrix dec...

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Abstract

The invention discloses a user behavior prediction and corresponding information recommendation method and device. Among them, based on the n original tags of each user in the benchmark user group, a multi-layer tag pair is generated for the user, and each user in the benchmark user group is divided into the multi-layer tag pair owned by the user to generate a multi-layer tag pair Grouping, and based on the user behavior in the multi-layer tag pair group corresponding to the multi-layer tag pair owned by the target user, predict the user behavior of the target user, and recommend corresponding information to the user according to the predicted result. Therefore, compared with the prediction method in the prior art, the prediction scheme of the present invention realizes layer-by-layer delivery of features by simply introducing multi-layer label pairs, thereby saving a lot of model training and machine resources, Improve the prediction and recommendation effect of actual application.

Description

technical field [0001] The present invention relates to the field of information technology, in particular to a method, device, computing device and storage medium for predicting user behavior and recommending corresponding information. Background technique [0002] In information applications, such as news and content aggregation apps, user behavior attributes need to be analyzed based on specific algorithms in order to recommend relevant information to users that matches their browsing behavior, that is, personalized recommendations for users. The core technology of personalized recommendation is the article click probability prediction algorithm. At present, the common practice is to use collaborative filtering, matrix decomposition, LR sorting algorithm, etc. These algorithms need to train models based on user behavior data. The above model training takes a lot of time and machine resources, and cannot be done in the case of insufficient data. [0003] In view of the a...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06F16/9535G06F16/9536G06F16/955
CPCG06F16/9535G06F16/955
Inventor 潘岸腾
Owner ALIBABA (CHINA) CO LTD
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