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Summary
This summary is machine-generated.

This study introduces a novel frequent sub-trajectory mining algorithm to analyze user behavior sequences and predict online purchases. It enhances trajectory similarity analysis by considering time and visit order, improving prediction accuracy.

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Area of Science:

  • Computational social science
  • Data mining
  • Behavioral analytics

Background:

  • User online access generates dynamic behavioral data, crucial for understanding online purchasing.
  • Existing computational methods struggle with measuring trajectory similarity and analyzing complex user behavior sequences.
  • Challenges include data reduction, spatial complexity, and potential prediction inaccuracies.

Purpose of the Study:

  • To develop a comprehensive computational method for measuring trajectory similarity in user behavior sequences.
  • To introduce a frequent sub-trajectory mining algorithm for enhanced trajectory analysis and prediction.
  • To address the challenges of detail balancing, data reduction, and prediction accuracy in behavioral data analysis.

Main Methods:

  • Evaluation of time-dimension similarity for user behavior sequence clustering.
  • Development of a frequent sub-trajectory mining algorithm prioritizing visit order.
  • Application of a variable-order Markov model to manage prediction model complexity.
  • Aggregation of time spent on web pages to improve prediction accuracy.

Main Results:

  • The study proposes a novel algorithm for frequent sub-trajectory mining, emphasizing the order of user visits.
  • A variable-order Markov model is employed to effectively manage the probability matrix size in forecasting.
  • Prediction accuracy is enhanced by incorporating aggregated time spent on specific web pages.

Conclusions:

  • The developed methods offer a more robust approach to analyzing user behavior trajectories and predicting online purchasing patterns.
  • The frequent sub-trajectory mining algorithm provides valuable insights into sequential user behavior.
  • The integration of time-dimension similarity and aggregated page time enhances the predictive power of user behavior models.