• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.2
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• pp.538-561
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• 2020

Recommender Systems (RecSys) have a major role in e-commerce for recommending products, which they may like for every user and thus improve their business aspects. Although many types of RecSyss are there in the research field, the state of the art RecSys has focused on finding the user similarity based on sequence (e.g. purchase history, movie-watching history) analyzing and prediction techniques like Recurrent Neural Network in Deep learning. That is RecSys has considered as a sequence prediction problem. However, evaluation of similarities among the customers is challenging while considering temporal aspects, context and multi-component ratings of the item-records in the customer sequences. For addressing this issue, we are proposing a Deep Learning based model which learns customer similarity directly from the sequence to sequence similarity as well as item to item similarity by considering all features of the item, contexts, and rating components using Dynamic Temporal Warping(DTW) distance measure for dynamic temporal matching and 2D-GRU (Two Dimensional-Gated Recurrent Unit) architecture. This will overcome the limitation of non-linearity in the time dimension while measuring the similarity, and the find patterns more accurately and speedily from temporal and spatial contexts. Experiment on the real world movie data set LDOS-CoMoDa demonstrates the efficacy and promising utility of the proposed personalized RecSys architecture.

• Journal of Information Technology Services
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• v.13 no.3
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• pp.309-321
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• 2014

Recently, due to the advent of ubiquitous computing and the spread of intelligent portable device such as smart phone, iPad and PDA has been amplified, a variety of services and the amount of information has also increased fastly. It is becoming a part of our common life style that the demands for enjoying the wireless internet are increasing anytime or anyplace without any restriction of time and place. And also, the demands for e-commerce and many different items on e-commerce and interesting of associated items are increasing. Existing collaborative filtering (CF), explicit method, can not only reflect exact attributes of item, but also still has the problem of sparsity and scalability, though it has been practically used to improve these defects. In this paper, using a implicit method without onerous question and answer to the users, not used user's profile for rating to reduce customers' searching effort to find out the items with high purchasability, it is necessary for us to analyse the segmentation of customer and item based on customer data and purchase history data, which is able to reflect the attributes of the item in order to improve the accuracy of recommendation. We propose the method of recommendation system using association rule and weighted preference so as to consider many different items on e-commerce and to refect the profit/weight/importance of attributed of a item. To verify improved performance of proposing system, we make experiments with dataset collected in a cosmetic internet shopping mall.

• Journal of Internet Computing and Services
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• v.8 no.6
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• pp.127-133
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• 2007

The u-learning environment has been developed through a number of iterations, and has now been formally evaluated, through analysis of student learning results and the use of quantitative and qualitative measures, Generally, for advance learning effect and analysis of student learning results, the most learning system be use to the item analysis method. But, nowadays, it has using the IRT(Item Response Theory) instead of the item analysis method, The IRT adopts explicit models for the probability of each possible response to a test. Therefore, I proposed the lightweight component based u-learning system using the IRT. Applied device of u-learning is PDA which is in Windows mobile 5.0 environments.

• Archives of design research
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• v.13 no.1
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• pp.49-58
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• 2000

This study is to implement the search system of human sensibility ergonomics in the internet shopping mall, which is a the electronic commerce in the contemporary as a shopping culture on the internet. Instead a category of business, an item, cost & size is using the keyword of a search in a existing shopping mall, the research is accomplished the center of system selecting products by the sensitivity feeling in products. The search system chooses the proper item and makes database with the sensible vocabulary for its image and then searches the item chosen by customers with keywords of the vocabulary after constructing web-server on the internet. This study - systematizes customers' sensible needs with more practical ways. - recognize the customers' sense on items and provides the applied technology conditions tor customers. - gives more opportunities of choice to customers on the internet shopping mall. - supplies various information and approaches to the customers' needs with practice.

• Journal of Korean Academy of Nursing
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• v.46 no.1
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• pp.109-117
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• 2016

Purpose: The aim of this study was to develop a Korean version of Night Eating Questionnaire (KNEQ) and test its psychometric properties and evaluate items according to item response theory. Methods: The 14-item NEQ as a measure of severity of the night eating syndrome was translated into Korean, and then this KNEQ was evaluated. A total of 1171 participants aged 20 to 50 completed the KNEQ on the Internet. To test reliability and validity, Cronbach's alpha, correlation, simple regression, and factor analysis were used. Each item was analyzed according to Rasch-Andrich rating scale model and item difficulty, discrimination, infit/outfit, and point measure correlation were evaluated. Results: Construct validity was evident. Cronbach's alpha was .78. The items of evening hyperphagia and nocturnal ingestion showed high ability in discriminating people with night eating syndrome, while items of morning anorexia and mood/sleep provided relatively little information. The results of item analysis showed that item2 and item7 needed to be revised to improve the reliability of KNEQ. Conclusion: KNEQ is an appropriate instrument to measure severity of night eating syndrome with good validity and reliability. However, further studies are needed to find cut-off scores to screen persons with night eating syndrome.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.12 no.2
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• pp.61-68
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• 2012

Recommender Systems have gained much popularity among researchers and is applied in a number of applications. The exponential growth of users and products poses some key challenges for recommender systems. Recommender Systems mostly suffer from scalability and accuracy. The accuracy of Recommender system is somehow inversely proportional to its scalability. In this paper we proposed a Context Aware Hybrid Recommender System using matrix reduction for Hybrid model and clustering technique for predication of item features. In our approach we used user item-feature rating, User Demographic information and context information i.e. specific time and day to improve scalability and accuracy. Our Algorithm produce better results because we reduce the dimension of items features matrix by using different reduction techniques and use user demographic information, construct context aware hybrid user model, cluster the similar user offline, find the nearest neighbors, predict the item features and recommend the Top N- items.

• Journal of Intelligence and Information Systems
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• v.22 no.2
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• pp.81-96
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• 2016

As the internet has become widespread and easy to access everywhere, it is common for people to search information via online search engines such as Google and Naver in everyday life. Recent studies have used online search volume of specific keyword as a measure of the internet users' attention in order to predict disease outbreaks such as flu and cancer, an unemployment rate, and an index of a nation's economic condition, and etc. For stock traders, web search is also one of major information resources to obtain data about individual stock items. Therefore, search volume of a stock item can reflect the amount of investors' attention on it. The investor attention has been regarded as a crucial factor influencing on stock price but it has been measured by indirect proxies such as market capitalization, trading volume, advertising expense, and etc. It has been theoretically and empirically proved that an increase of investors' attention on a stock item brings temporary increase of the stock price and the price recovers in the long run. Recent development of internet environment enables to measure the investor attention directly by the internet search volume of individual stock item, which has been used to show the attention-induced price pressure. Previous studies focus mainly on Dow Jones and NASDAQ market in the United States. In this paper, we investigate the relationship between the individual investors' attention measured by the internet search volumes and stock price changes of individual stock items in the KOSDAQ market in Korea, where the proportion of the trades by individual investors are about 90% of the total. In addition, we examine the difference between industries in the influence of investors' attention on stock return. The internet search volume of stocks were gathered from "Naver Trend" service weekly between January 2007 and June 2015. The regression model with the error term with AR(1) covariance structure is used to analyze the data since the weekly prices in a stock item are systematically correlated. The market capitalization, trading volume, the increment of trading volume, and the month in which each trade occurs are included in the model as control variables. The fitted model shows that an abnormal increase of search volume of a stock item has a positive influence on the stock return and the amount of the influence varies among the industry. The stock items in IT software, construction, and distribution industries have shown to be more influenced by the abnormally large internet search volume than the average across the industries. On the other hand, the stock items in IT hardware, manufacturing, entertainment, finance, and communication industries are less influenced by the abnormal search volume than the average. In order to verify price pressure caused by investors' attention in KOSDAQ, the stock return of the current week is modelled using the abnormal search volume observed one to four weeks ahead. On average, the abnormally large increment of the search volume increased the stock return of the current week and one week later, and it decreased the stock return in two and three weeks later. There is no significant relationship with the stock return after 4 weeks. This relationship differs among the industries. An abnormal search volume brings particularly severe price reversal on the stocks in the IT software industry, which are often to be targets of irrational investments by individual investors. An abnormal search volume caused less severe price reversal on the stocks in the manufacturing and IT hardware industries than on average across the industries. The price reversal was not observed in the communication, finance, entertainment, and transportation industries, which are known to be influenced largely by macro-economic factors such as oil price and currency exchange rate. The result of this study can be utilized to construct an intelligent trading system based on the big data gathered from web search engines, social network services, and internet communities. Particularly, the difference of price reversal effect between industries may provide useful information to make a portfolio and build an investment strategy.

• Journal of the Korean Society of Clothing and Textiles
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• v.30 no.6 s.154
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• pp.992-1000
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• 2006

The main purpose of this research is to develop the methodology of objective evaluation of clothing appearance by eye movement analysis. The visual clothing items used in this study were skirt, one-piece, pants and shirt with the style variation of silhouette and details. By observing eye movement during visual evaluation of clothing, we can achieve the basic fixation data of eye movement. Moreover, we developed the Matlab program to extract the fixation coordinate and number of eye fixation on each part of the clothing item. As results, there were differences in the duration of fixation time for each item and the fixation time was not different by styles within a clothing item. However, we could find differences in the fixation time within a style, in other words, we could select the important parts of the clothing by observing the fixation time in a certain clothing item. It is also noted that time required in visual information processing differs depending on the item, and there was a region which contain more information independent with styles in the same item. By developing the objective method of visual evaluation that correspond to human's visual information processing, the results are expected to be applied in the retrieval program in internet shopping mall or in the development of contents for advertisement of clothing.

• International Journal of Contents
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• v.5 no.1
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• pp.51-55
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• 2009

In this paper, we present a structure of personalized e-Learning system to study for a test formalized by uniform multiple-choice using on/off line mixed estimations as is the case of Driver :s License Test in Korea. Using the system a candidate can study toward the license through the Internet (and/or mobile instruments) within the personalized concept based on IRT(item response theory). The system accurately estimates user's ability parameter and dynamically offers optimal evaluation problems and learning contents according to the estimated ability so that the user can take possession of the license in shorter time. In order to establish the personalized e-Learning concepts, we build up 3 databases and 2 agents in this system. Content DB maintains learning contents for studying toward the license as the shape of objects separated by concept-unit. Item-bank DB manages items with their parameters such as difficulties, discriminations, and guessing factors, which are firmly related to the learning contents in Content DB through the concept of object parameters. User profile DB maintains users' status information, item responses, and ability parameters. With these DB formations, Interface agent processes user ID, password, status information, and various queries generated by learners. In addition, it hooks up user's item response with Selection & Feedback agent. On the other hand, Selection & Feedback agent offers problems and content objects according to the corresponding user's ability parameter, and re-estimates the ability parameter to activate dynamic personalized learning situation and so forth.

• Journal of the Korean Home Economics Association
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• v.42 no.1
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• pp.153-166
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• 2004

The purpose of this study was to examine the internet shoppers' new purchase behavior, to examine the general purchase behavior(i.e., purchase pattern, preference), and to examine the related factors to promotion strategies(i.e., e-mail, event) of internet shopping mall. Focus group interviews were done with 40 internet shopping-mall users on May, 2003 for the data collection. Data were analyzed by content analysis and descriptive statistics(i.e., frequency, percent). The results of this study were as following. First, competitive price, accurate product and service information and convenience were considered as important factors in the new purchase behavior among internet shoppers. Second, the more frequent purchasing time through the internet shopping mall were on weekdays rather than weeekends and the most preferred information search engine were category type, item type, and price type in order. Third, e-mails from internet shopping mall were most likely opened by internet shoppers, that is to say, e-mail can be the efficient communication tool as well as the possible promotion strategies. Specifically, the title of email was considered as an important factor to approach the target consumers.