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Probability matrix decomposition-based community trust recommendation method and system

A probabilistic matrix decomposition and recommendation method technology, which is applied in the field of community trust recommendation methods and systems based on probability matrix decomposition, can solve the problems of low accuracy of recommendation algorithms and the inability of recommendation algorithms to reduce the influence of trust relationships, and achieves improved accuracy. sexual effect

Inactive Publication Date: 2016-11-16
传化公路港物流有限公司 +1
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0006] The embodiment of the present invention provides a community trust recommendation method based on probability matrix decomposition, which aims to solve the problem that the existing recommendation algorithm based on probability matrix decomposition cannot reduce the influence of unilateral interaction relationship on the trust relationship and the accuracy of the recommendation algorithm is not high

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  • Probability matrix decomposition-based community trust recommendation method and system
  • Probability matrix decomposition-based community trust recommendation method and system
  • Probability matrix decomposition-based community trust recommendation method and system

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Embodiment 1

[0032] figure 1 The implementation process of the community trust recommendation method based on probability matrix decomposition provided by the embodiment of the present invention is shown, and the details are as follows:

[0033] S101. Obtain user behavior data, and classify users according to the behavior data to obtain a community set.

[0034] Wherein, the behavior data includes one or more of occupation, geographic location, purchase record and search record.

[0035] Generally speaking, user behavior data should be the personal data of users with certain differences in order to distinguish users from others, and because of this, the trust relationship between users with similar or identical behavior data will be higher than that of behavior data different users. In the embodiment of the present invention, users with similar or identical behavior data are classified into a community, and the trust relationship between users is also transformed into a community trust r...

Embodiment 2

[0053] figure 2 It shows the implementation process of obtaining user behavior data provided by the embodiment of the present invention, classifying users according to the behavior data, and obtaining community collections, and further includes the following steps:

[0054] S201. Use the K-means clustering method to perform cluster analysis on users to obtain a community set, and store the community attributes used to identify the community set in user information.

[0055] Commonly used classification algorithms include K-means clustering, ant colony algorithm, similarity calculation, etc. The embodiment of the present invention uses the K-means clustering method to perform cluster analysis on users by analyzing behavior data, so that each user has a corresponding community attribute.

[0056] At this time, since the community attribute belongs to the user feature that can identify the user, in the present invention, the community attribute is stored in the user information...

Embodiment 3

[0058] In an embodiment of the present invention, the probability decomposition matrix model includes:

[0059] User feature matrix P: used to record user information, consisting of l rows and n columns, where l represents the number of user information;

[0060] Project feature matrix Q: used to record project information, consisting of m rows and l columns;

[0061] User rating matrix R: used to record the rating data of users on items, consisting of m rows and n columns, matrix elements represents user u’s rating on item i, and satisfy:

[0062] r ^ ui = Q i P u ;

[0063] Among them, P u is the uth column of P, indicating the user information of user u, Q i is the i-th row of Q, indicating the item information of item i.

[0064] In the embodiment of the present invention, the user information may includ...

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Abstract

The invention is suitable for the field of social network information recommendation and provides a probability matrix decomposition-based community trust recommendation method and system. The method comprises the steps of obtaining behavior data of users, and obtaining a community set; building a community data model according to user information, a user relationship, project information required to be recommended and the community set, and obtaining a trust relationship among the users; building a probability matrix decomposition model according to the trust relationship, solving the model, and performing calculation to obtain recommendation information; and recommending required project information in the recommendation information to corresponding users according to a predetermined recommendation rule. According to embodiments of the method and the system, a community relationship of the users is introduced in the trust relationship, and the users in the same community are distinguished from the users in different communities, so that the trust relationship can reflect a real relationship among the users more accurately, the situation that a one-sided interactive relationship is regarded as the trust relationship is avoided, and the accuracy of a trust-based recommendation algorithm is greatly improved.

Description

technical field [0001] The invention belongs to the field of social network information recommendation, in particular to a community trust recommendation method and system based on probability matrix decomposition. Background technique [0002] Since the development of online social networks facilitates the acquisition and collection of user interaction data and social relationship data, how to use users' social network information to solve the problems existing in traditional recommendation algorithms has become a research hotspot. In order to obtain the interest characteristics of cold-start users, researchers began to use additional social information sources (friendship, group membership, and social trust) to improve recommendation accuracy. These algorithms can also be called social recommendation algorithms. At present, researchers have proposed many trust-based recommendation algorithms, which have proved that trust can also improve the performance of recommendation a...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F17/30G06Q50/00G06K9/62
CPCG06F16/9535G06Q50/01G06F18/23213
Inventor 张礼名李卫民李珣锋刘炜
Owner 传化公路港物流有限公司
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