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Data recommendation method and data recommendation system

A data recommendation and data technology, applied in marketing and other directions, can solve the problems of enhancing the stickiness and system robustness of the recommendation system

Inactive Publication Date: 2014-06-18
SHENGLE INFORMATION TECH SHANGHAI
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0013] The purpose of the present invention is to provide a data recommendation method and system, which can solve the problem of overfitting and user data sparsity using one-way absolute interest modeling, and enhance the stickiness of users to the recommendation system and the robustness of the system

Method used

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  • Data recommendation method and data recommendation system

Examples

Experimental program
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Embodiment 1

[0053] Such as figure 1 As shown, the present invention provides a data recommendation method, including:

[0054] Step S11, acquiring positive feedback data and negative feedback data from the user's historical behavior on the data. Specifically, the data are various types of information such as clothing information, etc. In this embodiment, the concept of negative feedback data generated by negative feedback behaviors is introduced in addition to positive feedback data generated by users' positive feedback behaviors.

[0055] Preferably, the positive feedback data includes one or any combination of data generated by the user's browsing, clicking, watching, purchasing, or high ratings, etc., which can express the user's interest in the data, and the negative feedback data includes One or any combination of the data generated by the user's behaviors such as skipping, canceling, ignoring, negating, or low ratings can reflect that the user has little interest in the data or has...

Embodiment 2

[0080] Such as Figure 4 As shown, the present invention also provides another data recommendation method. The difference between this embodiment and Embodiment 1 is that the data that the user has accessed are filtered out from the data to obtain the data that the user has not accessed. According to The feature value of each unvisited data and the user's interest weight of each data feature obtain the user's interest value for each unvisited data, so as to make the recommendation result more accurate. The method includes:

[0081] Step S21, acquiring positive feedback data and negative feedback data from the user's historical behavior on the data. Specifically, the data are various types of information such as clothing information, etc. In this embodiment, the concept of negative feedback data generated by negative feedback behaviors is introduced in addition to positive feedback data generated by users' positive feedback behaviors.

[0082] Preferably, the positive feedback...

Embodiment 3

[0099] Such as Figure 6 As shown, the present invention also provides another data recommendation system, including a data acquisition module 61 , a feature value module 62 , an interest weight module 63 , an interest value module 64 and a recommendation module 65 .

[0100] The data acquisition module 61 is used to acquire positive feedback data and negative feedback data from the user's historical behavior on data. Specifically, the data are various types of information such as clothing information, etc. In this embodiment, the concept of negative feedback data generated by negative feedback behaviors is introduced in addition to positive feedback data generated by users' positive feedback behaviors.

[0101] Preferably, the positive feedback data includes one or any combination of data generated by the user's browsing, clicking, watching, purchasing, or high ratings, etc., which can express the user's interest in the data, and the negative feedback data includes One or an...

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Abstract

The invention relates to a data recommendation method and a data recommendation system. The method comprises the following steps: positive feedback data and negative feedback data are acquired from historical behaviors of a user to data; the characteristic value of each data relative to each data characteristic is generated according to the characteristics of the data and preset data characteristics; the positive feedback data and the negative feedback data are randomly paired, and the interest weight of the user to each data characteristic is acquired according to the characteristic values of the positive feedback data and the negative feedback data as well as the paired positive feedback data and the paired negative feedback data; the interest value of the user to each data is acquired according to the characteristic value of the data and the interest weight of the user to each data characteristic; and the interest values are sorted from largest to smallest, and the first K data with the highest interest values is acquired and recommended to the user, wherein K is a positive integer. By adopting the data recommendation method and the data recommendation system of the invention, the problems of over fitting and user data sparsity caused by one-way absolute interest modeling can be solved, and the stickiness of users to the recommendation system and the robustness of the recommendation system are enhanced.

Description

technical field [0001] The invention relates to a data recommendation method and system. Background technique [0002] In today's Internet, massive data is not news. It is reported that all the printed information in human history is 200PB (1PB=1024TB=1048576GB), and the information processing of a single large-scale website has entered the PB era and will move forward to the EB (1EB=1024PB) era. With such a vast amount of information, if there is no suitable way and method to obtain information, it will not only make people helpless in front of the information, but even cause people's sense of depression and make it a burden. In order to solve the convenience and comfort of information acquisition, human beings are constantly exploring. The way to obtain information on the Internet has gradually shifted from the original stage of the portal era of categorizing and searching for information, to the search engine era of mesh-like correlation screening for various types of i...

Claims

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Application Information

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IPC IPC(8): G06Q30/02
Inventor 王文广陈运文
Owner SHENGLE INFORMATION TECH SHANGHAI
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