User preference analysis method and device based on big data

An analysis method and technology of an analysis device, applied in special data processing applications, electrical digital data processing, instruments, etc., can solve the problems of small sample size, manual analysis, low efficiency, etc., and achieve the effect of improving user experience and improving recommendation efficiency.

Active Publication Date: 2018-01-16
BEIJING JINGDONG SHANGKE INFORMATION TECH CO LTD +1
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Problems solved by technology

[0004] Among the above technologies, the LR logistic regression algorithm analysis method requires analysts to determine the coefficients of each feature based on business experience, which is strongly dependent on analyst experience, and each business requires manual analysis, which is inefficient and has a small number of samples
And because users have different preferences for content in different time periods, it is difficult to find the most suitable time weight function, so it is also difficult to accurately mine user preferences based on time weight statistical scoring method

Method used

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  • User preference analysis method and device based on big data
  • User preference analysis method and device based on big data
  • User preference analysis method and device based on big data

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Embodiment Construction

[0036] Example embodiments will now be described more fully with reference to the accompanying drawings. Example embodiments may, however, be embodied in many forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete and will fully convey the concept of example embodiments to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided in order to give a thorough understanding of embodiments of the present disclosure. However, those skilled in the art will appreciate that the technical solutions of the present disclosure may be practiced without one or more of the specific details being omitted, or other methods, components, devices, steps, etc. may be adopted. In other instances, well-known technical solution...

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Abstract

The disclosure provides a user preference analysis method and device based on big data. The method comprises the steps of: acquiring interactive behavior data of a user and a content, wherein the content has at least one tag; carrying out preprocessing on the interactive behavior data, generating a feature data set, and using the feature data set as an input feature value of a gcForest model; using a quasi probability vector output by each layer of cascaded forest in the gcForest model and features of the feature data set as input features of a next layer of cascaded forest; and according to aquasi probability vector output by a last layer of cascaded forest of the gcForest model, acquiring a preference probability of the user on the tag. The user preference analysis method provided by the disclosure can provide a more accurate user preference analysis result on the basis of big data samples.

Description

technical field [0001] The present disclosure relates to the technical field of machine learning, in particular, to a big data-based user preference analysis method and device. Background technique [0002] With the development of Internet technology, personalized content recommendation for users is becoming more and more popular. Taking article recommendation as an example, by setting one or more tags for each article according to the content of the article, and obtaining the user's operations on the article, it is possible to analyze which tags the user prefers, so that other articles under these tags can be recommended to the user , to improve user experience. [0003] In the existing personalized recommendation technology, the methods for analyzing user preferences mainly include the analysis method based on LR logistic regression algorithm and the statistical formula scoring method based on analyst strategy for each feature according to the time weight. In the analysi...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F17/30G06Q30/02G06N99/00
Inventor 王颖帅李晓霞苗诗雨
Owner BEIJING JINGDONG SHANGKE INFORMATION TECH CO LTD
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