Decoding Consumer Hearts: Advanced Algorithms Enha | newswise

Newswise – A recent study (DOI: 10.1016/j.dsm.2024.03.007) By Yuhan Dong from Huaisheng School of Public Administration, Nanyang Technological University, Singapore, published in the journal Data Science and Management On March 30, 2024, a user preference mining algorithm was introduced. This algorithm integrates data mining (DM) and social behavior (SB) analysis to improve brand building (BB) for SMEs. By more accurately predicting consumer preferences, this algorithm provides essential data support, enabling enterprises to optimize their branding strategies and achieve higher brand value.

The 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, which incorporates temporal behavior to address asynchrony issues in user data. It outperforms existing models such as computing power cost-aware online and lightweight deep pre-ranking systems (COLD) and multiple additive regression trees (MART) in terms of convergence, mean square error (MSE), and mean absolute error (MAE). Does. , The experimental results show an average area under the curve (AUC) value of 0.953 and an accuracy rate of 0.984, which is significantly higher than competing models. The efficiency of the model is 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 more accurately identify their target audiences, optimize product design, and tailor marketing strategies to effectively meet consumer needs.

Dr. Yuhan Dong, corresponding author and the driving force behind this research, emphasizes the potential of algorithms to revolutionize brand strategy. “Our model not only predicts consumer preferences with remarkable accuracy, but also adapts to constantly changing social dynamics, ensuring brands remain relevant and competitive.”

The implications of this research are far-reaching, providing small and medium-sized enterprises with 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 connect more deeply with their audiences. This data-driven approach promises to elevate brand building from an art to an exact science, fostering stronger consumer connections and fueling business growth.

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Reference

DOI

10.1016/j.dsm.2024.03.007

original source url

https://doi.org/10.1016/j.dsm.2024.03.007

About this Data Science and Management

Data Science and Management (DSM) 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 fields of business, economics, finance, operations, engineering, health care, transportation, agriculture Is. , Energy, Environment, Sports and Social Management. The DSM was launched in 2021, and is published quarterly by Xi’an Jiaotong University.