A Hybrid of Sequential Rules and Collaborative Filtering for Product Recommendation Export

@proceedings{citeulike:2949726, abstract = {Customers 'purchase behavior may vary over time. Traditional collaborative filtering (CF) methods make recommendations to a target customer based on the purchase behavior of customers whose preferences are similar to those of the target customer; however, the methods do not consider how the customers' purchase behavior may vary over time. Although the sequential rule method considers the sequence of customers' purchase behavior over time, it does not make use of the target customer's purchase data for the current period. To resolve the above problems, this work proposes a novel hybrid recommendation method that combines the segmentation-based sequential rule method with the segmentation-based CF method. Experiment results show that the hybrid method outperforms traditional CF methods.}, author = {Liu, Duen-Ren and Lai, Chin-Hui and Lee, Wang-Jung}, booktitle = {E-Commerce Technology and the 4th IEEE International Conference on Enterprise Computing, E-Commerce, and E-Services, 2007. CEC/EEE 2007. The 9th IEEE International Conference on}, citeulike-article-id = {2949726}, citeulike-linkout-0 = {http://dx.doi.org/10.1109/CEC-EEE.2007.6}, citeulike-linkout-1 = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=4285217}, doi = {10.1109/CEC-EEE.2007.6}, journal = {E-Commerce Technology and the 4th IEEE International Conference on Enterprise Computing, E-Commerce, and E-Services, 2007. CEC/EEE 2007. The 9th IEEE International Conference on}, keywords = {recommender, sequential\_pattern}, pages = {211--220}, posted-at = {2008-07-02 03:09:08}, priority = {4}, title = {A Hybrid of Sequential Rules and Collaborative Filtering for Product Recommendation}, url = {http://dx.doi.org/10.1109/CEC-EEE.2007.6}, year = {2007} }

TY - CONF ID - citeulike:2949726 L3 - citeulike-article-id:2949726 N2 - Customers 'purchase behavior may vary over time. Traditional collaborative filtering (CF) methods make recommendations to a target customer based on the purchase behavior of customers whose preferences are similar to those of the target customer; however, the methods do not consider how the customers' purchase behavior may vary over time. Although the sequential rule method considers the sequence of customers' purchase behavior over time, it does not make use of the target customer's purchase data for the current period. To resolve the above problems, this work proposes a novel hybrid recommendation method that combines the segmentation-based sequential rule method with the segmentation-based CF method. Experiment results show that the hybrid method outperforms traditional CF methods. JF - E-Commerce Technology and the 4th IEEE International Conference on Enterprise Computing, E-Commerce, and E-Services, 2007. CEC/EEE 2007. The 9th IEEE International Conference on EP - 220 TI - A Hybrid of Sequential Rules and Collaborative Filtering for Product Recommendation T2 - E-Commerce Technology and the 4th IEEE International Conference on Enterprise Computing, E-Commerce, and E-Services, 2007. CEC/EEE 2007. The 9th IEEE International Conference on SP - 211 KW - recommender KW - sequential_pattern AU - Liu, Duen-Ren AU - Lai, Chin-Hui AU - Lee, Wang-Jung PY - 2007/// UR - http://dx.doi.org/10.1109/CEC-EEE.2007.6 ER -