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Web service recommendation method based on CNN and LSTM

A recommendation method and web service technology, applied in the field of web service recommendation

Inactive Publication Date: 2020-12-15
HARBIN UNIV OF SCI & TECH
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

The traditional collaborative filtering technology occupies a high position in the application system, but there is still a problem of data sparsity

Method used

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  • Web service recommendation method based on CNN and LSTM
  • Web service recommendation method based on CNN and LSTM
  • Web service recommendation method based on CNN and LSTM

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Embodiment 1

[0059] Based on the problem of data sparsity in the application system of the traditional recommendation technology, the present invention provides a Web service recommendation method based on CNN and LSTM. Include the following steps:

[0060] Step 1. Calculate user preference features based on user implicit feedback information:

[0061] (1) Let the set of users be , the set of all services is ,use to represent the user for service rating;

[0062] (2) User Follow or track service , the service will be stored in the user 's focus set middle, means user attention set The total number of user-service interaction records in ;

[0063] (3) Consider the user Focus on focused web services When being followed by other users, the system will generate an interaction information record in the focus set of the current user. means user 's focus set Medium service The number of user-service interaction records followed by other users;

[0064] (4) Co...

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Abstract

The invention relates to a Web service recommendation method based on a CNN and LSTM. At present, a traditional collaborative filtering technology dominates an application system, but still has the problem of data sparsity. The Web service recommendation method based on the CNN and LSTM effectively combines the CNN with the LSTM to construct a deep learning model to realize an optimal recommendation result, and uses implicit feedback information, which is the historical behavior of a user, to extract the preference of the user when computing the preference characteristics of the user. A BERT-based language representation model word vectorization method is used for training natural language attributes of a user, an API and Mashup to obtain a feature matrix of each attribute, constructs a score prediction model based on the CNN and LSTM, and inputs each feature matrix into the score prediction model to obtain a prediction score of the Web service by the user, and a recommendation strategy selects the Web service with the user score Top_3 to generate recommendation for the user. The method is applied to the field of the Internet.

Description

technical field [0001] The invention relates to a method for recommending Web services based on CNN and LSTM. Background technique [0002] The rapid development of the Internet has led to a rapid increase in the number of candidate services that meet user needs. How to find Web services that meet user needs from a large-scale Web service collection has become a major research problem in the field of service computing. The traditional collaborative filtering technology occupies a high position in the application system, but there is still the problem of data sparsity. Contents of the invention [0003] The purpose of the present invention is to provide a method for recommending Web services based on CNN and LSTM, so as to alleviate the problem of sparsity of recommended data and improve the accuracy of recommendation. [0004] Above-mentioned purpose realizes by following technical scheme: [0005] A Web service recommendation method based on CNN and LSTM, which is chara...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F16/9535G06F40/30G06N3/04G06N3/08
CPCG06F16/9535G06F40/30G06N3/049G06N3/08G06N3/044G06N3/045
Inventor 赵悦张宏国马超
Owner HARBIN UNIV OF SCI & TECH
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