Mining the Up-to-Moment Preference Model based on Partitioned Datasets for Real Time Recommendation

The up-to-moment dataset is built by combining the past dataset and the recent dataset. The proposal is to compute association rules in real time. This study proposed the model, and algorithm that is sensitive to time. It can be utilized in real time by applying partitioned combination law after dividing the past dataset into(k-1). Also, we suggested applying the exponential smoothing method to When the association rules of were compared, The simulation results showed that is most accurate for testing dataset than and in huge dataset.