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Cross-dynamic filling method for relieving data sparsity problem in recommendation system

A data sparse, recommendation system technology, applied in data processing applications, buying/selling/lease transactions, commerce, etc., can solve problems such as difficulty in recommendation, alleviate the problem of data sparsity, improve personalized experience, and increase merchant revenue.

Inactive Publication Date: 2018-03-23
CHONGQING UNIV OF POSTS & TELECOMM
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Problems solved by technology

However, as the scale of the recommendation system continues to expand, the user rating data appears to be extremely sparse, which brings considerable difficulty to the recommendation. How to fill the sparse data has become our primary problem to solve

Method used

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  • Cross-dynamic filling method for relieving data sparsity problem in recommendation system
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  • Cross-dynamic filling method for relieving data sparsity problem in recommendation system

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Embodiment Construction

[0031] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0032] A cross dynamic filling method for alleviating the data sparsity problem in a recommendation system, comprising the following steps:

[0033] Step 1, perform data preprocessing on e-commerce data;

[0034] Wherein, the e-commerce data preprocessing process is as follows:

[0035] Input: raw e-commerce data;

[0036] Output: preprocessed expert data;

[0037] Step 1.1, extracting user-related static features and dynamic transaction behavior features to obtain a user data table;

[0038] Such as figure 1 As shown, the user data table is an example.

[0039] Step 1.2, extracting product-related features to obtain a product information table;

[0040] Such as figure 2 As shown, the product information table is an example.

[0041] Further, the construction of user-commodity-scoring matrix described in step 2

[0042] Such as image 3 As sh...

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Abstract

The invention discloses a cross-dynamic filling method for relieving a data sparsity problem in a recommendation system. The method comprises the steps of (1) preprocessing e-commerce user behavior data and obtaining a user information table and a product information table, (2) establishing a user-commodity-score matrix, (3) calculating a user similarity and a product similarity according to the established user information table and product information table, and (4) carrying out data sparsity processing: gradually filling the user-commodity-score matrix through a user similarity and productsimilarity cross-dynamic filling method. Compared with a single user and commodity similarity information filling method, the method has the advantages that the user similarity and the product similarity are skillfully combined, the user-commodity-score matrix can be filled well, and thus the data sparsity problem in recommendation can be relieved. An accurate recommendation can be carried out foreach user, a user personalized experience is improved, merchant sales are promoted, and merchant revenue is increased.

Description

technical field [0001] The invention belongs to the field of artificial intelligence, and relates to a cross dynamic filling method for alleviating the problem of data sparsity in a recommendation system. Background technique [0002] With the rapid development of Internet technology and various conveniences brought to people by e-commerce platforms, a large amount of e-commerce transaction data has been generated. How to dig out valuable information from these e-commerce data, make intelligent recommendations, and improve user experience has become a problem that we need to solve. However, with the continuous expansion of the scale of the recommendation system, the user rating data is extremely sparse, which brings considerable difficulty to the recommendation. How to fill the sparse data has become our primary problem to solve. Contents of the invention [0003] The present invention aims to solve the above problems, and proposes a cross dynamic filling method for allev...

Claims

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Application Information

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IPC IPC(8): G06Q30/02G06Q30/06
CPCG06Q30/0253G06Q30/0631
Inventor 黄梅根崔文豪周理含
Owner CHONGQING UNIV OF POSTS & TELECOMM
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