International Journal of u-and e- Service, Science and Technology 6 Evaluation results 6.1 Comparison Results In this paper, we compared the proposed method with two existing recommender ystems: user-based CF and item-based CF [6]. We divided the test data in the ratio of 80 to 20 to evaluate: 80 used for learning data, and 20 used for verification data. The existing systems make recommendation for each user with the top 30 items in the list of recommended items. And the proposed method use the top 3 of recommendation (the number of products is 30) generated at the end of each transaction The comparison is executed in terms of the accuracy and the diversity of the ecommendation. The results of comparison are presented in Table 2. According to the experiment result, our proposed method is better than existing method in precision 8-10% and in coverage 11-17% Table 1. The comparison results between proposed method and existing method Coverage 0.3889 6.2 Measuring the Accuracy of Real-time Recommendation frequeney af clickstream Fig. 4. An evaluation result for accuracy of real-time recommendation o measure the accuracy of the systems real-time recommendation, we divide the data by 80: 20. And we select the user data who s transactions was occurred over 15 Then we generate recommendation using each users recent 3 transaction data and measure the accuracy of recommendation based on the items in the top 2 individuals The result is presented in Figure 4. The results show as user's real-time information as increase: the accuracy of the reco dation also seems to rise6 Evaluation Results 6.1 Comparison Results In this paper, we compared the proposed method with two existing recommender systems: user-based CF and item-based CF [6]. We divided the test data in the ratio of 80 to 20 to evaluate: 80 used for learning data, and 20 used for verification data. The existing systems make recommendation for each user with the top 30 items in the list of recommended items. And the proposed method use the top 3 of recommendation (the number of products is 30) generated at the end of each transaction. The comparison is executed in terms of the accuracy and the diversity of the recommendation. The results of comparison are presented in Table 2. According to the experiment result, our proposed method is better than existing method in precision 8~10% and in coverage 11-17%. Table 1. The comparison results between proposed method and existing method Method Precision Coverage User-based CF 0.3889 0.7564 Item-based CF 0.4017 0.8156 Proposed method 0.4820 0.9242 6.2 Measuring the Accuracy of Real-time Recommendation Fig. 4. An evaluation result for accuracy of real-time recommendation To measure the accuracy of the system's real-time recommendation, we divide the data by 80:20. And we select the user data who`s transactions was occurred over 15. Then we generate recommendation using each user's recent 3 transaction data and measure the accuracy of recommendation based on the items in the top 2 individuals. The result is presented in Figure 4. The results show as user's real-time information was increase; the accuracy of the recommendation also seems to rise. International Journal of u- and e- Service, Science and Technology 15