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Data Mining Method of Intelligent Market Management Based on Collaborative Recommendation Algorithm.

Ziling Wu1,2, Shi Wang2, Yuanhang Cai3

  • 1School of Finance and Economics, Hainan Vocational University of Science and Technology, Haikou 571126, China.

Computational Intelligence and Neuroscience
|September 23, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces an intelligent market management data mining method using collaborative filtering to enhance recommendation accuracy. The proposed algorithm significantly improves upon traditional methods, addressing issues of low coverage and high error rates.

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Area of Science:

  • Data Mining
  • Recommender Systems
  • Machine Learning

Background:

  • Traditional market management data recommendation algorithms suffer from low coverage and accuracy.
  • High mean absolute error and root mean square error are prevalent issues in existing methods.

Purpose of the Study:

  • To propose an intelligent market management data mining method based on a collaborative recommendation algorithm.
  • To enhance the accuracy and reduce errors in market management data recommendations.

Main Methods:

  • Utilizes data mining for preprocessing market management data.
  • Implements a collaborative filtering recommendation algorithm based on user preferences.
  • Employs a graph neural network to mine user-project information.

Main Results:

  • The proposed algorithm, AGCAN, demonstrated significant accuracy improvements on MovieLens-1M, MovieLens-100K, and Amazon-baby datasets compared to benchmark algorithms.
  • Achieved accuracy gains ranging from 0.1% to 51.17% across different datasets and comparisons.
  • The graph neural network approach effectively mines interdependencies between users and projects.

Conclusions:

  • The intelligent data mining method based on collaborative filtering offers a superior approach to market management data recommendation.
  • The algorithm shows strong performance across various benchmark datasets, indicating its generalizability.
  • Future work should incorporate additional personalized user information, such as interests and purchase history, to further refine recommendations.