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UrduThe study proposes a user preference mining algorithm that combines DM and SB to analyze and predict consumer preferences with high accuracy. The algorithm employs a cross-domain strategy, incorporating temporal behaviors to address asynchrony issues in user data. It outperforms existing models such as the computing power cost-aware online and lightweight deep pre-ranking system (COLD) and multiple additive regression tree (MART) in terms of convergence, mean square error (MSE), and mean absolute error (MAE). The experimental results reveal a mean area under the curve (AUC) value of 0.953 and an accuracy rate of 0.984, significantly higher than the competing models. The model’s efficiency is further demonstrated through its practical application in predicting user brand preferences with an average error of only 0.11. By analyzing user data from both social media and e-commerce platforms, the algorithm can accurately predict consumer preferences, providing valuable insights for brand development. This innovative approach enables enterprises to identify their target audience more precisely, optimize product designs, and tailor marketing strategies to meet consumer needs effectively.
Dr. Yuhan Dong, the corresponding author and driving force behind this research, emphasizes the algorithm’s potential to revolutionize brand strategy. “Our model not only predicts consumer preferences with remarkable accuracy but also adapts to the ever-changing social dynamics, ensuring that brands stay relevant and competitive.”
The implications of this research are far-reaching, offering small and medium-sized enterprises a powerful tool to enhance their brand value. By understanding consumer preferences at a granular level, businesses can tailor their products and marketing strategies to resonate more deeply with their audience. This data-driven approach promises to elevate brand building from an art to a precise science, fostering stronger consumer connections and driving business growth.
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References
DOI
Original Source URL
https://doi.org/10.1016/j.dsm.2024.03.007
About Data Science and Management
Data Science and Management (DSM) is a peer-reviewed open access journal for original research articles, review articles and technical reports related to all aspects of data science and its application in the field of business, economics, finance, operations, engineering, healthcare, transportation, agriculture, energy, environment, sports, and social management. DSM was launched in 2021, and published quarterly by Xi’an Jiaotong University.
