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

Jeong-Hye Han; Lu-Na Byon

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

Jeong-Hye Han, Lu-Na Byon, Journal of Internet Computing and Services, Vol. 8, No. 2, pp. 105-116, Apr. 2007
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Keywords: Real-Time Recommendation, Temporal Association Rules, Up-to-Moment Dataset, Partitioned Combination Law, Exponential Smoothing Method

Abstract

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, $EM_{past'}$ 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 $EM^{ES}_{past}$ applying the exponential smoothing method to $EM^p_{past'}$ When the association rules of $EM_{past'}\;EM^w_{past'\;and\;EM^{ES}_{past}$ were compared, The simulation results showed that $EM^{ES}_{past}$ is most accurate for testing dataset than $EM_{past}$ and $EM^w_{past}$ in huge dataset.

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Cite this article

[APA Style]

Han, J. & Byon, L. (2007). Mining the Up-to-Moment Preference Model based on Partitioned Datasets for Real Time Recommendation. Journal of Internet Computing and Services, 8(2), 105-116.

[IEEE Style]

J. Han and L. Byon, "Mining the Up-to-Moment Preference Model based on Partitioned Datasets for Real Time Recommendation," Journal of Internet Computing and Services, vol. 8, no. 2, pp. 105-116, 2007.

[ACM Style]

Jeong-Hye Han and Lu-Na Byon. 2007. Mining the Up-to-Moment Preference Model based on Partitioned Datasets for Real Time Recommendation. Journal of Internet Computing and Services, 8, 2, (2007), 105-116.