Improved user recommendation method based on neighbor project slope one algorithm

A technology for recommending methods and projects, applied in computing, data processing applications, computer components, etc., can solve the problems of not considering the similarity between projects, inconvenient practical application, project scoring deviation, etc., to improve algorithm efficiency, reduce The effect of sparsity and high prediction accuracy

Inactive Publication Date: 2016-06-15
DALIAN UNIV
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Problems solved by technology

[0003] In the past, the algorithm design only improved the problem of item scoring deviation, without considering the s

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  • Improved user recommendation method based on neighbor project slope one algorithm
  • Improved user recommendation method based on neighbor project slope one algorithm
  • Improved user recommendation method based on neighbor project slope one algorithm

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Embodiment 1

[0031] The present invention will be further described below in conjunction with accompanying drawing.

[0032] The 337 investigation refers to the US International Trade Commission's 337 investigation under Section 337 of the "1930 Tariff Act" of the United States. The investigation conducted by the case prohibits all acts of unfair competition or any unfair trade practices in exporting products to the United States. In recent years, in order to maintain their dominance in the US market, multinational companies in the US have conducted a large number of 337 investigations on companies entering the US market, and the trend is increasing year by year. If the Section 337 investigation is established, the exporter's products may be permanently excluded from the US market, which will cause huge economic losses. Analyzing and mining the information of Act 337 through the recommendation system to predict whether a company is subject to patent investigation and the type of patent in...

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Abstract

The invention, which relates to the analysis field of the user-project data, designs an improved recommendation method based on combination of a nearest neighbor clustering algorithm and a slope one algorithm. According to the invention, the method is based on combination of the k-means clustering method and the slope one algorithm. The k-means clustering method is used for finding similar individual projects for a user to form a neighbor project set; a user-project investigation data table is calculated by using the slope one algorithm; and then data prediction is carried out and the data are recommended to the user. When possibility of project investigation on a user is determined, similarity between the data projects is taken into consideration, so that the algorithm prediction result becomes accurate. A predication investigation possibility problem is converted into a project recommendation problem for solution. The possibility of investigation on the user is predicted based on the neighbor project slope one algorithm, so that the user can take related measures in advance.

Description

technical field [0001] The invention relates to a user improvement recommendation method based on the adjacent item SlopeOne algorithm. Specifically, it analyzes a large number of user-item data by combining data to predict the data, which belongs to the field of data mining and data analysis. Background technique [0002] Compared with traditional processing methods based on evolutionary algorithms, collaborative filtering algorithms have great advantages in solving the problems of recommendation and prediction. First, the algorithm uses the user's historical behavior to analyze user preferences, and then recommends products or information to the user. The slopeone algorithm is a collaborative filtering algorithm based on score prediction, which uses the user's score deviation for a group of items and the user's score for some items to predict the user's score for other items. This algorithm does not calculate the similarity between items, and uses the simplest linear regr...

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Application Information

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IPC IPC(8): G06Q10/04G06Q10/06G06K9/62
CPCG06Q10/04G06Q10/063G06F18/23213
Inventor 张强黄丽鹏车超魏小鹏
Owner DALIAN UNIV
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