A Hybrid Weighted Periodical Pattern Mining and Prediction for Personal Mobile Commerce

Provided by: The World
Topic: Data Management
Format: PDF
In case of large amount of the search engine based applications, mobile e-commerce has established a more interest under both industry and academia. From that mining the user behavior and prediction of the user to analysis the mobile commerce behaviors based on their actions are most important. To perform these steps, previous work proposed a novel structure called Mobile Commerce Explorer (MCE). It can be performed in three ways: Similarity Inference Model (SIM) for measuring the similarities amongst stores and items, Personal Mobile Commerce Pattern Mine (PMCP-Mine) algorithm for well-organized discovery of mobile users Personal Mobile Commerce Patterns (PMCPs); and Mobile Commerce Behavior Predictor (MCBP) for prediction of possible mobile user behaviors.

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