Handling sequential pattern decay: Developing a two-stage collaborative recommender system |
| |
Authors: | Cheng-Lung Huang Wei-Liang Huang |
| |
Affiliation: | Department of Information Management, National Kaohsiung First University of Science and Technology, 2, Juoyue Rd., Nantz District, Kaohsiung 811, Taiwan, ROC |
| |
Abstract: | This study proposes a sequential pattern based collaborative recommender system that predicts the customer’s time-variant purchase behavior in an e-commerce environment where the customer’s purchase patterns may change gradually. A new two-stage recommendation process is developed to predict customer purchase behavior for the product categories, as well as for product items. The time window weight is introduced to produce sequential patterns closer to the current time period that possess a larger impact on the prediction than patterns relatively far from the current time period. This study is the first to propose time-decaying sequential patterns within a collaborative recommender system. The experimental results show that the proposed system outperforms the traditional collaborative system using a public food mart dataset and a synthetic dataset. |
| |
Keywords: | |
本文献已被 ScienceDirect 等数据库收录! |
|