A Hybrid of Sequential Rules and Collaborative Filtering for Product Recommendation

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.