원문정보
초록
영어
Existing recommender systems generate recommendation usually using user's information previously collected. The information reflects the user`s tastes, but it doesn`t include user`s intend at that time. So existing recommender systems sometimes generate not suitable recommendation because of difference between user`s current purpose and the information of past time. In this paper, we propose genetic recommend generating method for overcome this problem. Our method analyzes user`s real-time click-stream for grabbing user`s current intention, then uses genetic algorithm for generating appropriate recommendation. To reflect user`s real-time intention, the proposed method adapts fitness function of genetic algorithm continuously. To evaluate the proposed approach, we compare the proposed method with existing CF methods using the web-server log data collected from Internet jewelry shop. And we confirm that the proposed approach can generate more accurate recommendation then compared methods.
목차
1 Introduction
2 User Model
3 Genetic Recommendation Generating Method
3.1 About the Genetic Algorithm
3.2 Chromosome Encoding
3.3 Population
3.4 A Fitness Function
3.5 Genetic Operations
3.6 Stopping Criteria
4 Real-time Fitness Function Adaption
5 Evaluation Environments
6 Evaluation Results
6.1 Comparison Results
6.2 Measuring the Accuracy of Real-time Recommendation
7 Conclusions and Future Work
References