Personalized u-commerce recommending service using weighted sequential pattern with time-series and FRAT method

This paper proposes a new personalized u-commerce recommending service using weighted sequential pattern with time-series and FRAT(Frequency, Regency, Amount and Type of merchandise or service) method under ubiquitous computing environment which is required by real time accessibility and agility. In this paper, using an implicit method without onerous question and answer to the users, it is necessary for us to make the FRAT score and the task of mining sequential pattern with time-series in order to do recommending service based on periodicity analysis by timely changing trends of seasonable pattern, and to improve the accuracy of recommendation with high purchasability To verify improved performance of proposing system, we make experiments with dataset collected in a cosmetic internet shopping mall.