A Collaborative Filtering-based Recommendation System with Relative Classification and Estimation Revision based on Time

Abstract

In the recommendation system that recommends services to a specific user by using the estimation value of other users for users' recommendation service, collaborative filtering methods are widely used. But such recommendation systems have problems that exact classification is not possible because a specific user is classified to already classified group in the course of clustering and inexact result can be recommended in case of big errors in users' estimation values. In this paper, in order to increase estimation accuracy, the researchers suggest a recommendation system that applies collaborative filtering after reclassifying on the basis of a specific user's classification items and then finding and correcting the estimation values of the users beyond the critical value of time. This system uses a method where a specific user is not classified to already classified group in the course of clustering but a group is reorganized on the basis of the specific user. In addition, the researchers correct estimation information by cutting off the subordinate 10% from the trimmed mean of samples and then applies weight over time to the remaining data. As the result of an experiment, the suggested method demonstrated about 14.9%'s more accurate estimation result in case of using MAE than general collaborative filtering method.