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Distributed Intelligent Optimization of E-commerce User Purchase Data Mining using Spark Framework

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Yayımlandı:Informatica vol. 48, no. 20 (Dec 2024), p. 29
Yazar: Wu, Jianjun
Baskı/Yayın Bilgisi:
Slovenian Society Informatika / Slovensko drustvo Informatika
Konular:
Accuracy
Simulation
Search engines
Data mining
Clothing
Energy management
Searching
Optimization
Topology
Revenue
Design
Algorithms
Data collection
Quality of service
Error analysis
Shopping
Stability
Electronic commerce
Privacy
Optimization algorithms
Distributed processing
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Özet:As the development of e-commerce becomes more and more intelligent, higher requirements have been put forward for the algorithms controlling e-commerce operations. However, the current e-commerce operation is not timely and accurate enough to update the purchase data and statistics, resulting in cost consumption and revenue is not proportional, and can not accurately meet the user favorite. To speed up the collection of user purchase behavior data and improve the revenue of e-commerce operations, the study introduces adaptive degree values based on a distributed computing framework combined with a topological structure. The computing framework is used to speed up the calculation and convergence of user data, and the topology is responsible for classifving the data in the dataset and calculating the optimal location. The improved algorithm under the control of the topology structure, the accuracy of the product is above 94%, the highest is above 98%, compared with other algorithms, the accuracy is higher. The data collected on JD shopping platform shows that compared with other algorithms, the improved algorithm is improved by 81.2% due to the stability of the fitness value. In the simulation experiment, the overlap between the noise value of beauty search 2000-2700 and the noise value of clothing matching 2000-2500 in the shopping platform was large. Therefore, there was a correlation between the user's search for clothing collocation and the beauty search. In summary the improved algorithm is highly effective in both stability, accuracy, and applied error control. Therefore, the study of the improved algorithm has a better application for data mining of user purchase behavior.
ISSN:0350-5596
1854-3871
DOI:10.31449/inf.v48120.6779
Kaynak:Advanced Technologies & Aerospace Database