Cloud Computing Product Service Scheme Recommendation System Based on a Hierarchical Knowledge Graph

It is difficult for users to understand the complex cloud product information for product selection. Using this information to recommend satisfactory cloud products is a challenge. Previous studies focused on similar information of users and products while neglecting relevance; therefore, they could...

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Bibliographic Details
Published in:IEEE access 2023, Vol.11, p.120541-120553
Main Authors: Xu, Shulin, Wu, Ziyang, Shi, Chunyu, Sun, Mengyu
Format: Article
Language:English
Subjects:
Cloud computing
cloud product functionality dataset
cloud service
Companies
Data models
Electronic commerce
Filtering
Industries
knowledge graph
Knowledge graphs
Knowledge representation
PageRank
Product information
Product set recommendation
Recommender systems
Search algorithms
Weight
Citations: Items that this one cites
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Summary:It is difficult for users to understand the complex cloud product information for product selection. Using this information to recommend satisfactory cloud products is a challenge. Previous studies focused on similar information of users and products while neglecting relevance; therefore, they could not create recommendation approaches that account for functional dependencies among cloud products. To overcome this challenge, this study proposes a cloud product set recommendation model based on a hierarchical knowledge graph (KG) with a pre-post correlation of product functionality. There are two main contributions: First, we constructed a cloud product functionality and performance KG using the dependency information of layers and entities to represent complicated pre-post logical connections. The KG was designed according to the cloud service model. Second, we designed an improved PageRank algorithm to obtain the importance weight for each functionality and performance, which replaces the original average method with the proportion of connection weight. We considered the release time of the functionality, launch time of the product, and last update time of the product as crucial factors in the recommendation score to reflect the importance of the functionality and current development stage of the product. Finally, our method recommended a product set based on the weighted scores from the above results. In addition, we constructed a cloud product functionality dataset containing 339 functionalities. The experimental results show that the proposed method can generate a closely related set of products, leading to improved accuracy and higher satisfaction compared to mainstream methods.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2023.3328217