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Resource individuation recommendation method based on user relevance

A recommendation method and user technology, applied in special data processing applications, instruments, electronic digital data processing, etc., can solve the problems of high dimension of target user score matrix, obliteration of user personalization, and huge amount of calculation data.

Inactive Publication Date: 2012-10-24
UNIV OF ELECTRONICS SCI & TECH OF CHINA
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  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] 1. In the actual business system, due to the large amount of resources and user data, the dimension of the target user scoring matrix constructed by Slope One is very high, and the amount of calculation data is huge
[0006] 2. Slope One predicts the ratings of target users through a "universal average" idea, but this "universal average" obliterates user personalization, the accuracy of rating prediction is not high, and affects the quality of resource recommendations

Method used

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  • Resource individuation recommendation method based on user relevance
  • Resource individuation recommendation method based on user relevance
  • Resource individuation recommendation method based on user relevance

Examples

Experimental program
Comparison scheme
Effect test

Embodiment

[0025] figure 1 It is a functional block diagram of a specific embodiment of the resource personalized recommendation method based on user relevance in the present invention.

[0026] In this example, if figure 1 As shown, the resource personalized recommendation method based on user relevance includes the following four parts:

[0027] (1) Similar user mining, that is, analyzing and mining frequent sets of target users

[0028] Using the user's historical scoring records for resources as a data source, use user association rules to analyze and mine multiple target user frequent sets whose support meets the requirements;

[0029] Among them, the user association rule means that the target user and other users have scored one or more resources, and the number of resources that both the target user and other users have scored is the support degree;

[0030] The target user frequent set includes the item set and support degree composed of the target user and other users;

[0...

example

[0042] The following abbreviated example illustrates the invention.

[0043] In this example, the resource is a video resource, and the score is the user's rating of the video resource after watching it.

[0044] The historical scoring records of users on video resources constitute a database, which is the historical scoring data of 5 users on 4 video resources, as shown in Table 1:

[0045]

[0046] Table 1

[0047] In this example, as shown in Table 1, user set: {U 1 ,U 2 ,U 3 ,U 4 ,U 5}, rating set range: {1,2,3,4,5}. In Table 1, Null means that the user has not rated the corresponding video resource and has not visited the corresponding video resource.

[0048] 1. Use user association rules to analyze and mine frequent sets of target users

[0049] In this example, it is first necessary to preprocess the user's historical rating records for resources. Only the ratings with a user rating greater than or equal to 3 points can be used for association rule analysis ...

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Abstract

The invention discloses a resource individuation recommendation method based on user relevance. The method comprises firstly using a user relevance rule mining technique for analyzing history grading records of a user on resources and excavating a frequent set of a target user; then selecting one target user frequent set which is maximum in number of terms and highest in support to build an interest similar group of the target user; inputting history grades of the user in the interest similar group of the target user on the resources in a Slope One algorithm to serve as core data, and conducting grade forecast on resources without visiting of the target user; and finally recommending the resources without visiting and with a grade predicted value larger than the threshold value of the target user to the target user according to the value. Users with similar interests of the target user are used for forecasting in a process of grade forecast of the Slope One algorithm on the resources without visiting of the target user, grade matrix dimensionality of the target user and intermediately calculated data quantity are reduced, and accuracy rate of the grade forecast is improved.

Description

technical field [0001] The invention belongs to the technical field of network application personalized recommendation, and specifically relates to a resource personalized recommendation method based on user relevance. Background technique [0002] With the rapid development of the Internet, the information resources in the network are becoming more and more abundant, which makes people worry about the scarcity of resources and the trouble of resource overload. Faced with massive resource information, it is often difficult for users to find the most suitable or most interesting resource. Therefore, in order to solve the problem of obtaining user preference information from massive resources, resource personalized recommendation systems are widely used in major commercial websites. [0003] Collaborative filtering is currently the most widely used and successful personalized recommendation system. It aims to approximate the target user's rating of a resource based on the ra...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F17/30
Inventor 孙健艾丽丽谢发川隆克平周伟强李江华王雁峰林威黎焯泓唐明
Owner UNIV OF ELECTRONICS SCI & TECH OF CHINA
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