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18 November 2024 Research and analysis of decision-making algorithm based on consuming users in B2C rankings
Wenjuan Zhou
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Proceedings Volume 13403, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2024) ; 134030J (2024) https://doi.org/10.1117/12.3051650
Event: International Conference on Algorithms, High Performance Computing, and Artificial Intelligence, 2024, Zhengzhou, China
Abstract
Although search recommendation algorithms have been widely used, they also face a large bottleneck: it is difficult to improve recommendation accuracy and reduce user cost-effectiveness. Leaderboards adopts the popular goods priority recommendation strategy, without considering the user behavior data in the decision-making process, as well as differentiation factors between users make recommendations less effective, which in turn affects the conversion rate. Therefore, this paper proposes a personalized recommendation algorithm framework based on the B2C list, on the one hand, by considering the consumer user's current behavioral data to carry out the development trend of predictive recommendation, on the other hand, by information such as the average price and quantity of each major category of goods purchased by the consumer to categorize users, and then combined with the user's preference for the cross-information on the commodity to carry out the differentiation of commodity recommendation, so as to formulate a popular commodities list. The proposed framework has been analyzed and validated through legal advice website data to ensure its feasibility, but further analysis and research is needed due to the large number of B2C industry categories and the complexity of the data.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
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Wenjuan Zhou "Research and analysis of decision-making algorithm based on consuming users in B2C rankings", Proc. SPIE 13403, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2024) , 134030J (18 November 2024); https://doi.org/10.1117/12.3051650
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KEYWORDS
Data modeling
Analytical research
Statistical analysis
Algorithm development
Decision making
Data mining
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