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A method to alleviate data sparsity in recommendation system based on step-by-step dynamic filling

A recommendation system and data sparse technology, applied in data processing applications, complex mathematical operations, instruments, etc., can solve problems such as sparse scoring data matrix, inaccurate merchant recommendation lists, etc., achieve accurate similarity calculation, increase the number of common scores, Fill full effect

Active Publication Date: 2021-12-17
CHONGQING UNIV OF POSTS & TELECOMM
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  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

In the field of gourmet merchant recommendation, due to the huge number of gourmet merchants on the website, but only a few users are willing to give ratings to the gourmet merchants they have been to, so the user-food merchant rating data matrix is ​​extremely sparse, resulting in a collaborative filtering algorithm. The recommended list of merchants obtained is not accurate enough

Method used

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  • A method to alleviate data sparsity in recommendation system based on step-by-step dynamic filling
  • A method to alleviate data sparsity in recommendation system based on step-by-step dynamic filling
  • A method to alleviate data sparsity in recommendation system based on step-by-step dynamic filling

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Experimental program
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Embodiment 1

[0047] On the basis of the traditional collaborative filtering recommendation algorithm based on users and items, the embodiment of the present invention adds the consideration of the average value of the historical common rating difference of users and businesses to the selection of similar neighbor sets, and finally dynamically and step-by-step. The unrated data in the business rating matrix is ​​filled, and the method includes the following steps:

[0048] 101: Preprocess the user behavior data, and establish a user-food business rating matrix;

[0049]102: Construct a collection of historical scoring records for each user and merchant; construct a user collection, and sort the users in the user collection according to the number of merchants rated by users from large to small;

[0050] 103: Set user similarity threshold α and user history common score difference mean threshold β;

[0051] 104: According to the order of users in the user set, take a target user, calculate ...

Embodiment 2

[0062] The scheme in embodiment 1 will be further introduced below in conjunction with specific calculation formulas and experimental data, see the following description for details:

[0063] 201: For the user behavior data obtained from the American review website yelp, only select the relevant information of food merchants and their users, such as figure 1 , figure 2 shown;

[0064] Create a user-food business rating matrix, such as image 3 shown.

[0065] 202: Construct a user's historical scoring record set I(u) for each user, and construct a merchant's historical scoring record set U(i) for each merchant;

[0066] Build a user set U, count the number of merchants rated by each user, and sort the users in the user set according to the number of merchants rated by users from large to small;

[0067] 203: Set user similarity threshold α and user common score difference mean threshold β;

[0068] 204: Select the target item, calculate the similarity of user ratings, an...

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Abstract

The invention requests protection of a method for alleviating data sparsity in a recommendation system based on step-by-step dynamic filling. The method firstly preprocesses the user behavior data, and establishes the user-gourmet merchant scoring matrix; secondly, selects the target user to calculate the user similarity, and selects the user whose similarity is greater than the threshold α as the preselected similar neighbor user of the target user; then, calculates the user common score The difference mean value, select the pre-selected neighbor users whose common score difference mean value is less than the threshold β as the final similar neighbor users, and use the neighbor users to fill the scoring matrix in the first step; finally, for the remaining unfilled data in the scoring matrix, similar The degree threshold method and the mean value of the common rating difference are used to select similar merchants, and the second step is used to fill the scoring matrix with similar merchants. The present invention alleviates the sparsity problem of scoring matrix in the gourmet business recommendation system to a certain extent, and improves the accuracy of the recommendation system.

Description

technical field [0001] The invention belongs to the technical field of food recommendation, and in particular relates to a method for alleviating data sparsity in a recommendation system based on step-by-step dynamic filling. Background technique [0002] With the rapid development of Internet technology, the explosive growth of data scale has brought about a serious problem of "information overload". How to quickly and effectively obtain valuable information from complex data has become a key problem in the development of big data. In order to solve this problem, personalized recommendation algorithms have been extensively studied, the most common of which is collaborative filtering algorithm. In the field of gourmet merchant recommendation, due to the huge number of gourmet merchants on the website, but only a few users are willing to give ratings to the gourmet merchants they have been to, so the user-food merchant rating data matrix is ​​extremely sparse, resulting in a...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06Q10/06G06F17/16
CPCG06F17/16G06Q10/06393
Inventor 黄梅根王渝周理含
Owner CHONGQING UNIV OF POSTS & TELECOMM
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