  Recommender systems are mostly well known for their applications in
e-commerce sites and are mostly static models. Classical personalized
recommender algorithm includes item-based collaborative filtering method
applied in Amazon, matrix factorization based collaborative filtering algorithm
from Netflix, etc. In this article, we hope to combine traditional model with
behavior pattern extraction method. We use desensitized mobile transaction
record provided by T-mall, Alibaba to build a hybrid dynamic recommender
system. The sequential pattern mining aims to find frequent sequential pattern
in sequence database and is applied in this hybrid model to predict customers'
payment behavior thus contributing to the accuracy of the model.
