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Multi Markov chain-based content recommendation method

A Markov chain, content recommendation technology, applied in special data processing applications, instruments, business and other directions, can solve the problem that user items cannot be fully recommended, and cannot overcome the scalability and sparsity problems of recommendation algorithms. Achieve the effect of improving scalability, solving sparsity problems, and improving accuracy

Inactive Publication Date: 2010-09-08
NANJING UNIV
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AI Technical Summary

Problems solved by technology

[0010] The problem to be solved by the present invention is that the existing methods of personalized recommendation technology have deficiencies in varying degrees, the items that users may be interested in cannot be fully recommended, and the scalability and sparsity problems in the recommendation algorithm cannot be overcome

Method used

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  • Multi Markov chain-based content recommendation method

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Embodiment Construction

[0063] The content recommendation method based on the Markov chain and combined with user background information of the present invention can be applied to e-commerce and social networking sites. It can provide users with interested items and links, facilitate users to browse the website, increase the order rate of e-commerce websites, and improve the intelligence of applications.

[0064] Explanation of terms involved in the present invention:

[0065] 1) Markov model: It includes three parts: transition matrix, initial state, and represented user set. It is represented by the triplet MC(A, λ, G).

[0066] In the transition matrix A, each page X represents a state of the model, and X t Indicates the current state, X t-1 It means the state at the previous moment, let P ij =(X t =x j |X t-1 =x i ), 0ij represented by state x i transition to state x j The probability of , when the user pointed to by A has not clicked on the page X t , appears P t1 , P t2 ,...P tn ,...

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Abstract

The invention discloses a multi Markov chain-based content recommendation method. The method comprises the following steps of: establishing Markov models by using information of click stream of a user and establishing a user relationship matrix by using the background information of the user; combining the similar Markov models; and filling sparse items in the zero line of the combined Markov model according to the click stream of the similar user aggregates obtained by the user relationship matrix. The content recommendation method is individualized information recommendation technology on the network, and by the method, the interesting commodity and information are recommended to the user according to the characteristics of the interest, the behavior and the personal information. The interesting information and commodity are recommended to the user in a vast database, so that the browsing time is reduced, the problems of less user rating items and more sparse items in the collaborative recommendation are solved and the accuracy of the recommendation is improved.

Description

technical field [0001] The invention relates to the technical field of personalized recommendation, which recommends interested commodities and information to users according to their interest characteristics, behaviors and personal data. Personalized recommendation is based on massive data mining and is often used in e-commerce and social network applications. It can recommend information and products of interest to users in huge data, reducing browsing time. The present invention is specifically a content recommendation method based on Markov chain combined with user background information. Background technique [0002] Personalized recommendation technology is a technology with great application value. In recent years, personalized recommendation technology has been continuously applied by various e-commerce websites and social websites to provide users with the information and commodities they are interested in. Personalized recommendation technology was first proposed...

Claims

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Application Information

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IPC IPC(8): G06F17/30G06Q30/00G06Q30/02
Inventor 陈振宇封煜佳王浩然刘嘉吴一帆
Owner NANJING UNIV
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