• Journal of applied mathematics & informatics
• /
• v.28 no.1_2
• /
• pp.439-450
• /
• 2010

In this paper, we propose a new idea to evaluate the prediction accuracy of user's preference generated by memory-based collaborative filtering algorithm before prediction process in the recommender system. Our analysis results show the possibility of a pre-evaluation before the prediction process of users' preference of item's transaction on the web. Classification functions proposed in this study generate a user's rating pattern under certain conditions. In this research, we test whether classification functions select users who have lower prediction or higher prediction performance under collaborative filtering recommendation approach. The statistical test results will be based on the differences of the prediction accuracy of each user group which are classified by classification functions using the generative probability of specific rating. The characteristics of rating patterns of classified users will also be presented.

• Korean Business Review
• /
• v.20 no.2
• /
• pp.105-118
• /
• 2007

Recommender system is the tool for E-commerce company based on the internet for increasing their sales ratio in the market. Recommender system suggests the list of items which night be wanted by customers. This list generated by the result of customers' preference prediction through the prediction algorithm automatically. Recommender system will be able to offer not only the important information for marketing strategy but also reduce the cost of customers' information retrieval trough the analysis of customers' purchase patterns and features. But there are several problems like as the extension of the users and items scales and if the recommendation to customers generated by unreliable recommender system makes the customer royalty to the system to weaken. In this study, we propose the criterion for pre-evaluation on the prediction performance only using the preference ratings on the items which are rated by customers before prediction process and we study the features of customers who are classified through this classification criterion.

• Asia pacific journal of information systems
• /
• v.17 no.4
• /
• pp.187-206
• /
• 2007

The development of computer and information technology has been combined with the information superhighway internet infrastructure, so information widely spreads not only in special fields but also in the daily lives of people. Information ubiquity influences the traditional way of transaction, and leads a new E-commerce which distinguishes from the existing E-commerce. Not only goods as physical but also service as non-physical come into E-commerce. As the scale of E-Commerce is being enlarged as well. It keeps people from finding information they want. Recommender systems are now becoming the main tools for E-Commerce to mitigate the information overload. Recommender systems can be defined as systems for suggesting some Items(goods or service) considering customers' interests or tastes. They are being used by E-commerce web sites to suggest products to their customers who want to find something for them and to provide them with information to help them decide which to purchase. There are several approaches of recommending goods to customer in recommender system but in this study, the main subject is focused on collaborative filtering technique. This study presents a possibility of pre-evaluation for the prediction performance of customer's preference in collaborative filtering before the process of customer's preference prediction. Pre-evaluation for the prediction performance of each customer having low performance is classified by using the statistical features of ratings rated by each customer is conducted before the prediction process. In this study, MovieLens 100K dataset is used to analyze the accuracy of classification. The classification criteria are set by using the training sets divided 80% from the 100K dataset. In the process of classification, the customers are divided into two groups, classified group and non classified group. To compare the prediction performance of classified group and non classified group, the prediction process runs the 20% test set through the Neighborhood Based Collaborative Filtering Algorithm and Correspondence Mean Algorithm. The prediction errors from those prediction algorithm are allocated to each customer and compared with each user's error. Research hypothesis : Two research hypotheses are formulated in this study to test the accuracy of the classification criterion as follows. Hypothesis 1: The estimation accuracy of groups classified according to the standard deviation of each user's ratings has significant difference. To test the Hypothesis 1, the standard deviation is calculated for each user in training set which is divided 80% from MovieLens 100K dataset. Four groups are classified according to the quartile of the each user's standard deviations. It is compared to test the estimation errors of each group which results from test set are significantly different. Hypothesis 2: The estimation accuracy of groups that are classified according to the distribution of each user's ratings have significant differences. To test the Hypothesis 2, the distributions of each user's ratings are compared with the distribution of ratings of all customers in training set which is divided 80% from MovieLens 100K dataset. It assumes that the customers whose ratings' distribution are different from that of all customers would have low performance, so six types of different distributions are set to be compared. The test groups are classified into fit group or non-fit group according to the each type of different distribution assumed. The degrees in accordance with each type of distribution and each customer's distributions are tested by the test of ${\chi}^2$ goodness-of-fit and classified two groups for testing the difference of the mean of errors. Also, the degree of goodness-of-fit with the distribution of each user's ratings and the average distribution of the ratings in the training set are closely related to the prediction errors from those prediction algorithms. Through this study, the customers who have lower performance of prediction than the rest in the system are classified by those two criteria, which are set by statistical features of customers ratings in the training set, before the prediction process.

• Transactions of the Korean Society of Automotive Engineers
• /
• v.6 no.5
• /
• pp.146-152
• /
• 1998

Recently, for the development of vehicle structures and components there is a tendency to increase using numerical simulation methods compared with practical tests for the estimation of the fatigue strength. In this study, an integrated powerful methodology is suggested for fatigue strength evaluation through development of the interface program to integrate dynamic analysis quasi-static stress analysis and fatigue analysis, which were so far used independently. To verify the presented evaluation method, a single and zigzag bump run test, 4-post road load simulation and driving durability test have been performed. The prediction results show a good agreement between analysis and test. This research indicates that the integrated life prediction methodology can be used as a reliable design tool in the pre-prototype and prototype development stage, to reduce the expense and time of design iteration.

• International Journal of Computer Science & Network Security
• /
• v.21 no.3
• /
• pp.177-184
• /
• 2021

All over the world, people are affected by many chronic diseases and medical practitioners are working hard to find out the symptoms and remedies for the diseases. Many researchers focus on the feature detection of the disease and trying to get a better health recommendation system. It is necessary to detect the features automatically to provide the most relevant solution for the disease. This research gives the framework of Health Recommendation System (HRS) for identification of relevant and non-redundant features in the dataset for prediction and recommendation of diseases. This system consists of three phases such as Pre-processing, Feature Selection and Performance evaluation. It supports for handling of missing and noisy data using the proposed Imputation of missing data and noise detection based Pre-processing algorithm (IMDNDP). The selection of features from the pre-processed dataset is performed by proposed ensemble-based feature selection using an expert's knowledge (EFS-EK). It is very difficult to detect and monitor the diseases manually and also needs the expertise in the field so that process becomes time consuming. Finally, the prediction and recommendation can be done using Support Vector Machine (SVM) and rule-based approaches.

• Journal of the Korean Data and Information Science Society
• /
• v.18 no.3
• /
• pp.619-628
• /
• 2007

This study is devoted to suggesting the norm of detection abnormal users who are inferior to the other users in the recommender system compared with estimation accuracy. To select the abnormal users, we propose the pre-filtering method by using the preference ratings to the item rated by users. In this study, the experimental result shows the possibility of detecting the abnormal users before the process of preference estimation through the prediction algorithm. And It will be possible to improve the performance of the recommender system by using this detecting norm.

• Korean Journal of Radiology
• /
• v.20 no.4
• /
• pp.649-661
• /
• 2019

Objective: To systematically review the evaluation of the diagnostic accuracy of pre-treatment apparent diffusion coefficient (ADC) and change in ADC during the intra- or post-treatment period, for the prediction of locoregional failure in patients with head and neck squamous cell carcinoma (HNSCC). Materials and Methods: Ovid-MEDLINE and Embase databases were searched up to September 8, 2018, for studies on the use of diffusion-weighted magnetic resonance imaging for the prediction of locoregional treatment response in patients with HNSCC treated with chemoradiation or radiation therapy. Risk of bias was assessed by using the Quality Assessment Tool for Diagnostic Accuracy Studies-2. Results: Twelve studies were included in the systematic review, and diagnostic accuracy assessment was performed using seven studies. High pre-treatment ADC showed inconsistent results with the tendency for locoregional failure, whereas all studies evaluating changes in ADC showed consistent results of a lower rise in ADC in patients with locoregional failure compared to those with locoregional control. The sensitivities and specificities of pre-treatment ADC and change in ADC for predicting locoregional failure were relatively high (range: 50-100% and 79-96%, 75-100% and 69-95%, respectively). Meta-analytic pooling was not performed due to the apparent heterogeneity in these values. Conclusion: High pre-treatment ADC and low rise in early intra-treatment or post-treatment ADC with chemoradiation, could be indicators of locoregional failure in patients with HNSCC. However, as the studies are few, heterogeneous, and at high risk for bias, the sensitivity and specificity of these parameters for predicting the treatment response are yet to be determined.

• Fire Science and Engineering
• /
• v.3 no.1
• /
• pp.19-28
• /
• 1989

It has been recognized that the escape facility planning is very important for effective evacuation of accupants on fire event. The ultimate goal of the escape facility planning is to evacuate occupants rapidly from building fires to the safe areas. In fire event, occupants usually gather, utilize and finally act upon information about state transient of building fire system, which is consisted of components of fire, building and accupant during the ralatively short period of the fire event. That is, occupants' egress behavior is largely dependent upon building fire system. Therefore, comprehensive study for the relationship between building fire system and occupants' egress behavior is needed. This study aims to suggest the pre -occupancy evaluation method of the life safety performance for the architectural design based on prediction of occupants' egress behavior during building fires with computer simulation.

• Proceedings of the Korea Concrete Institute Conference
• /
• 2000.04a
• /
• pp.847-852
• /
• 2000

This paper presents an evaluation of the behavior and strength of two pre-tensioned concrete deep beams tested to failure with using the nonlinear strut-tie model approach. In the approach, the effective prestressing forces represented be equivalent external loads are gradually introduced along its transfer length in the nearest strut-tie model joints, the friction at the interface of main diagonal shear cracks is modeled by diagonal struts along the direction of the cracks in strut tie-model, and additional positioning of concrete ties a the place of steel ties is incorporated. Through the analysis of pre-tensioned concrete deep beams, the nonlinear strut-tie model approach proved to present effective solutions for prediction the essential aspects of the behavior and strength of pre-tensioned concrete deep beams.

• Fire Science and Engineering
• /
• v.3 no.2
• /
• pp.11-19
• /
• 1989

In this study, the independent variables are the floor plan configulation. The dependent variables are the occupant's egress behavior, especially spatial movement pattern, and life - safety performance of building. Fire events were simulated on single story of office building. Simulation run for allowable secaping thime(180 seconds) arbitrarily selected, and involved 48 occupants. The major findings Pre as follows. 1) Computer simulation model suggested in this study can be used as the Preoccupancy evaluation method of the life-safety performance for architectural design based on prediction of occupants' egress behavior in the levels of validity and sensitivity, 2) Sucess or failure in occupants' escape is determined by decreasing walking speed caused by jamming at exits or over crowded corridor, and increasing route length caused by running about in confusion at each subdivision and corridor. 3) In floor plan configuration which safe areas located at the extreme ends of the corridor, cellular floor planning have to be avoided preventing jamming and running about in confusion at overcrowded corridor.