Article information clustering method, article information pushing method and article information clustering device

A technology of item information and clustering method, applied in the field of item information clustering method, push method and device, can solve the problem of low clustering accuracy, not considering the time point of browsing different item information, and unable to truly reflect user needs and other problems to achieve the effect of the accuracy of the clustering results

Pending Publication Date: 2021-11-09
BEIJING WODONG TIANJUN INFORMATION TECH CO LTD +1
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] The existing item information clustering methods do not consider the time point of browsing different item information and the impact on clustering
Therefore, the existing item information clustering methods have low clustering accuracy and often cannot truly reflect user needs.

Method used

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  • Article information clustering method, article information pushing method and article information clustering device
  • Article information clustering method, article information pushing method and article information clustering device
  • Article information clustering method, article information pushing method and article information clustering device

Examples

Experimental program
Comparison scheme
Effect test

specific Embodiment approach 1

[0147] Using the sample data or the browsing time points of the item information included in each sample group and the state transition information of multiple item information, calculate the transition time difference between every two item information;

[0148] The transfer time difference is given by the above-mentioned embodiment Calculated, it can be expressed as That is, the transfer time difference from the vth item information i to the uth item information j after arbitrarily sorting the item information in the sample data; t v，u Characterize the transition probability relation P that is substituted into the transition probability matrix at row v and column u vu The transfer time difference in (t); Representation represents the average transfer time required to transfer from item information i to item information j through sample data statistics; N represents the total number of sample groups included in the sample data; k represents the kth sample group in the ...

specific Embodiment approach 2

[0149] Calculate the transition time difference between every two pieces of item information by using the new browsing time points of multiple pieces of item information counted within the set time period and the new state transition information of multiple pieces of item information;

[0150] The set time period is any time period in practical application, and it is generally a time period other than collecting sample data.

[0151] In the second embodiment, the Calculated, t gd 'Represents the transfer time difference required to transfer from item information g to item information d among the multiple pieces of item information counted within the set time period; M represents the total number of items that belong to the user within the set time period number; y represents the y-th user among all the users whose multiple pieces of item information are counted in the set time period; g→d represents the transfer from browsing item information g to browsing item information d...

specific Embodiment approach 3

[0152] Use the browsing time point of the item information included in each sample group, the state transition information of multiple item information, the new browsing time point of the multiple item information counted within the set time period, and the new browsing time point of the multiple item information. The state transition information of each item is calculated to calculate the transfer time difference between each two item information.

[0153] The third embodiment can be obtained by summing the result of the above-mentioned specific embodiment 1 and the result of the specific embodiment 2, namely t gd "=t gd '+t g，d , where t gd ' is obtained by the second embodiment above; t g，d is obtained from the above-mentioned specific embodiment 1, t g，d Characterizes the average transition time required to transition from item information g to item information d in sample data or item information included in each sample group.

[0154] In the third embodiment, the sa...

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Abstract

The invention discloses an article information clustering method and device and an article information pushing method and device, and relates to the technical field of computers. A specific embodiment of the method comprises the steps of collecting sample data, wherein the sample data comprises browsing time points of multiple pieces of article information and state transition information of the multiple pieces of article information; according to the browsing time point and the state transition information, calculating a relevancy matrix of the multiple pieces of article information; decomposing a corresponding feature vector for each piece of article information from the relevancy matrix; and according to the feature vector corresponding to each piece of article information, carrying out clustering operation on the plurality of pieces of article information. According to the embodiment, the clustering accuracy can be effectively improved, so that the user demand is truly reflected.

Description

technical field [0001] The present invention relates to the field of computer technology, in particular to a method for clustering item information, a push method and a device. Background technique [0002] Clustering and dividing commodities is not only helpful to the management of commodities, but also a marketing method. For example: displaying product information belonging to the same category or product information of the same cluster under the same column at the front end can attract customers to browse or purchase under the same column after browsing (or purchasing) a product related products. [0003] In the course of realizing the present invention, the inventor finds that there are at least the following problems in the prior art: [0004] The existing item information clustering methods do not take into account the time point of browsing different item information and the impact on clustering. Therefore, the existing item information clustering methods have low...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62G06Q30/02G06Q30/06H04L29/08
CPCG06Q30/0202G06Q30/0201G06Q30/0631H04L67/55G06F18/2321
Inventor 邓小龙高伟张光宇
Owner BEIJING WODONG TIANJUN INFORMATION TECH CO LTD
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