Context recommending method based on dynamic incremental updating

A technology of incremental update and recommended methods, which is applied in special data processing applications, instruments, electrical digital data processing, etc., and can solve problems such as uncomfortable users and the background of rapid information expansion

Active Publication Date: 2015-09-30
RENMIN UNIVERSITY OF CHINA
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AI Technical Summary

Problems solved by technology

However, these algorithms are based on static data and are not suitable for the background of rapid expansion of users and information in the current recommendation system.

Method used

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  • Context recommending method based on dynamic incremental updating
  • Context recommending method based on dynamic incremental updating
  • Context recommending method based on dynamic incremental updating

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Embodiment Construction

[0053] The present invention will be described in detail below in conjunction with the accompanying drawings. However, it should be understood that the accompanying drawings are provided only for better understanding of the present invention, and they should not be construed as limiting the present invention.

[0054] Such as figure 1 As shown, the context recommendation method based on dynamic incremental update of the present invention includes the following steps:

[0055] 1. Extract user information based on the historical records of the e-commerce website, and construct a tensor according to the user information; and perform tensor decomposition operation on the constructed tensor to obtain four types of feature factors. The specific process is as follows:

[0056] 1.1) On an e-commerce website (such as Taobao.com), user information is extracted through historical records. User information includes three categories, namely user name, item name, and user context informati...

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Abstract

The invention relates to a context recommending method based on dynamic incremental updating. The context recommending method is characterized by comprising the steps that step1, user information is extracted according to history records of an electronic commerce website, and a tensor is constructed according to the user information, wherein tensor decomposition calculation is performed on the constructed tensor so that four kinds of feature factors can be obtained; step2, when the scale of the tensor changes, a new tensor is constructed according to newly added data, and dynamic incremental updating is performed on the four kinds of feature factors obtained in step1 so that four kinds of new feature factors can be obtained; step3, when objects are recommended to a user, scoring values between the user and the objects are calculated, and the objects, serving as objects possibly liked by the user, with the high scoring values are recommended to the user. The context recommending method can be widely applied to a recommending system of the electronic commerce website.

Description

technical field [0001] The invention relates to a context recommendation method, in particular to a context recommendation method based on dynamic incremental update. Background technique [0002] After entering the 21st century, various e-commerce websites have developed rapidly. At the same time, with the development of various wireless technologies, the mobile Internet, the Internet of Things, and social networks have also developed very rapidly. All mankind has entered the era of "big data". In the "big data era", the problem of "information overload" is even more serious. The research and development of recommendation systems have solved the problem of information overload. With the development of "big data" such as social networks, there are more and more types of data. As one of the characteristics of big data, "Variety" has increasingly become a feature of recommendation system data. Faced with such a variety of data types, the recommendation method based on context...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F17/30
CPCG06F16/2358G06F16/24575G06F16/9535
Inventor 李翠平邹本友陈红谭力文
Owner RENMIN UNIVERSITY OF CHINA
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