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Matrix decomposition recommending method and system based on extended label

A matrix decomposition and matrix technology, which is used in data processing applications, sales/lease transactions, instruments, etc.

Active Publication Date: 2017-10-10
HAINAN UNIVERSITY
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] The object of the present invention is to solve the sparsity problem and the cold start problem of item label in matrix factorization method

Method used

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  • Matrix decomposition recommending method and system based on extended label
  • Matrix decomposition recommending method and system based on extended label
  • Matrix decomposition recommending method and system based on extended label

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0096] The present invention provides a matrix decomposition recommendation method and system based on extended tags. It should be noted that the steps shown in the flow chart of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and, although A logical order is shown in the flowcharts, but in some cases the steps shown or described may be performed in an order different from that shown or described herein.

[0097] The method embodiment provided in Embodiment 1 of the present application may be executed in a mobile terminal, a computer terminal, or a similar computing device. Take running on a computer terminal as an example, figure 1 It is a block diagram of the hardware structure of a computer terminal based on an extended tag-based matrix decomposition recommendation method according to an embodiment of the present invention. Such as figure 1 As shown, the computer terminal 100 may include one or more (only o...

Embodiment 2

[0216] Figure 9 is a structural block diagram of a matrix factorization recommendation system based on extended tags provided by an embodiment of the present invention, such as Figure 9 As shown, the present invention also provides a matrix decomposition recommendation system based on extended tags, including the following modules:

[0217] Tag similarity calculation module 201, for constructing item-label matrix based on item tag data and calculating tag similarity;

[0218] A first vector construction module 202, configured to construct a first item pair-label vector based on the item-label matrix;

[0219] The second vector construction module 203 is configured to expand the first item pair-label vector into a second item pair-label vector according to the label similarity;

[0220] An item similarity calculation module 204, configured to calculate item similarity based on the second item pair-label vector, and establish an item similarity matrix;

[0221] The implicit...

Embodiment 3

[0234] The invention also provides a storage medium. Optionally, in this embodiment, the above-mentioned storage medium may be used to store program codes executed by a method for recommending matrix factorization based on extended tags in the above-mentioned embodiment.

[0235] Optionally, in this embodiment, the storage medium is configured to store program codes for performing the following steps:

[0236] The first step is to construct an item-label matrix based on the item label data and calculate the label similarity;

[0237] The second step is to construct a first item pair-label vector based on the item-label matrix;

[0238] The third step is to expand the first item pair-label vector into a second item pair-label vector according to the label similarity;

[0239] The fourth step is to calculate the item similarity based on the second item pair-label vector, and establish an item similarity matrix;

[0240] The fifth step is to solve the user implicit feature mat...

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Abstract

The invention discloses a matrix decomposition recommending method based on an expanded label, comprising: constructing an item-label matrix based on the item label data and calculating the label similarity; constructing a first item pair-label vector based on the item-label matrix; and according to the label similarity, extending the first item pair label-vector as a second item pair-label vector; establishing an item similarity matrix based on the second item pair-label vector; solving a user implicit characteristics matrix and an item implicit characteristics matrix based on the item similarity matrix and the pre-constructed item scoring matrixes; predicting the score by the user to the unrated items; and building a list of recommended items for the user. At the same time, the invention also discloses a matrix decomposition recommending system based on the expanded label. The invention can provide the user with more accurate personalized recommendations when the label is sparse, and relieves the problem of cold starting of the item to a certain extent.

Description

technical field [0001] The invention relates to the technical field of personalized recommendation, in particular to a matrix decomposition recommendation method and system based on extended tags. Background technique [0002] In recent years, more and more recommendation systems have added the function of collaborative annotation. The emergence of Collaborative Tagging System (Collaborative Tagging System) promotes the degree of interaction between users and the system. The labeling system allows users to select labels that summarize resource characteristics to label resources according to their own understanding. In the tag system, users can use tags to mark the characteristics and categories of resources, so tags can reflect the characteristics of resources to a certain extent, and resources can be classified more accurately by analyzing the tag information of resources. At the same time, because the difference performance of user-labeled tags reflects different persona...

Claims

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Application Information

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IPC IPC(8): G06Q30/06
CPCG06Q30/0631
Inventor 黄梦醒韩惠蕊张雨冯文龙
Owner HAINAN UNIVERSITY
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