A Recommender System in U-Commerce Based on a Segmentation Method

It has been increasing the number and types of products on the u-commerce market and promoting interest in associated items rapidly. Therefore, agility and real time accessibility are crucial element in u-commerce. Existing collaborative filtering(CF) adopts evaluation methods based on personal profiles. However, these methods have been identified with difficulties in accurately analyzing the customers' tendencies and level of interest, as well as the problems of cost, consequently leaving customers unsatisfied. This paper proposes a new method of recommender system in u-commerce using cluster analysis of segmented merchandise to have different weights based on FRAT(Frequency, Recency, Amount and Type) method. Instead of using user's profiles for rating, we used an implicit method. It is necessary for us to make the task of preprocessing such as keeping FRAT method and clustering of merchandise category in order to recommend items with the profit/weight/importance of segmented merchandise. To verify improved performance of our proposing system compared to the previous system. We have conducted experiments with the same dataset, which was collected from a cosmetic web shopping mall.