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Mining method of computer data for recommendation systems

A data mining and recommendation system technology, applied in computing, digital data processing, special data processing applications, etc., can solve problems such as long time, achieve the effect of less reading times, fast data mining speed, and small storage space

Active Publication Date: 2012-10-24
TSINGHUA UNIV
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

When the user and product feature matrices become larger, the PMF algorithm in a stand-alone environment takes too long

Method used

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  • Mining method of computer data for recommendation systems
  • Mining method of computer data for recommendation systems
  • Mining method of computer data for recommendation systems

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Experimental program
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Embodiment Construction

[0043] The computer data mining method that the present invention proposes for recommendation system comprises the following steps:

[0044] (1) Set an N×M preference matrix R, where N is the number of rows of the preference matrix R, N is equal to the number of users, M is the number of columns of the preference matrix R, and M is equal to the number of items serving users;

[0045] (2) Input the file to the computer, and convert the input file into a sequence file in the mapping simplified model, so that each row in the sequence file is a row vector of the preference matrix R, and the data structure of each row of the preference matrix R is: row Composed of vector subscripts and key-value pair arrays, where the key-value pair array includes the service item number and the user's preference for the service item;

[0046] The data structure of each row of the preference matrix R is shown in the following table, where user i has a total preference for m service items:

[0047]...

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Abstract

The invention relates to a mining method of computer data for recommendation systems and belongs to the technical field of computer data processing. The method includes that user preference matrixes and service project preference matrixes are initialized in a main server of a computer, row vectors of preference matrixes input by a user are distributed to a plurality of mappers in the computer, all mappers calculate sub-directions of gradient directions of the user preference matrixes and the service project preference matrixes respectively, calculated results are sent to a simplifier in the computer, the simplifier receives and accumulating the sub-directions of the gradient directions, and the user preference matrixes and the service project preference matrixes are updated according to gradient direction matrixes of the user preference matrixes and the service project preference matrixes. According to the method, the existing probabilistic matrix factorization (PMF) algorithm is modified, large-scale data processing capabilities are improved; and preference matrixes are stored by data storage structures of key-value pairs, so that occupied storage space is small, and reading speed of data are rapid.

Description

technical field [0001] The invention relates to a computer data mining method used in a recommendation system, belonging to the technical field of computer data processing. Background technique [0002] With the rapid development of the Internet, the scale of e-commerce continues to expand, and the number and types of commodities increase rapidly, customers need to spend more and more time to find the commodities they want to buy from a large number of commodities. This process of browsing a large amount of irrelevant information and products makes consumers who are plagued by information overload continue to lose. In order to solve these problems, personalized recommendation system came into being. The personalized recommendation system is based on massive data mining technology. By analyzing user data, such as interests and preferences, it provides fully personalized decision support and information services, and improves the traffic conversion rate of e-commerce websites...

Claims

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Application Information

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IPC IPC(8): G06F17/30G06F17/16
Inventor 王建民丁贵广龙明盛姜晓伟
Owner TSINGHUA UNIV
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