• 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.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.7
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• pp.2840-2861
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• 2020

The purpose of this survey-based study is to make an analysis of search engine users' behaviors on the Search Engine Results Pages (SERPs) based on the three demographic characteristics gender, age, and program studying. In this study, a questionnaire was designed with 12 closed-ended questions. Remaining questions other than the demographic characteristic related ones were about "tab", "advertisement", "spelling suggestion", "related query suggestion", "instant search suggestion", "video result", "image result", "pagination" and the amount of clicking results. The questionnaire was used and the data collected were analyzed with the descriptive statistics as well as the inferential statistics. 84.2% of the study population was reached. Some of the major results are as follows: Most of each demographic characteristic category (i.e. female, male, under-20, 20-24, above-24, English computer engineering, Turkish computer engineering, software engineering) have rarely or more click for tab, spelling suggestion, related query suggestion, instant search suggestion, video result, image result, and pagination. More than 50.0% of female category click advertisement rarely; however, for the others, 50.0% or more never click advertisement. For every demographic characteristic category, between 78.0% and 85.4% click 10 or fewer results. This study would be the first attempt with its complete content and design. Search engine providers and researchers would gain knowledge to user behaviors about the usage of the SERPs based on the demographic characteristics.

• Journal of Korea Society of Digital Industry and Information Management
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• v.19 no.4
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• pp.53-71
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• 2023

This study explores the relationship between various user factors and the level of trust in ChatGPT, a sophisticated language model exhibiting human-like capabilities. Specifically, we considered demographic characteristics such as age, education, gender, and major, along with factors related to previous AI experience, including duration, frequency, proficiency, perception, and familiarity. Through a survey of 140 participants, comprising 71 females and 69 males, we collected and analyzed the data to see how these user factors have a relationship with trust in ChatGPT. Both descriptive and inferential statistical methods, encompassing multiple linear regression models, were employed in our analysis. Our findings reveal significant relationships between user factors such as gender, the perception of prior AI interactions, self-evaluated proficiency, and Trust in ChatGPT. This research not only enhances our understanding of trust in artificial intelligence but also offers valuable insights for AI developers and practitioners in the field.

• The Journal of Society for e-Business Studies
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• v.19 no.3
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• pp.125-141
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• 2014

Nowadays, overflowing data produced every second from the internet make people to be difficult to search for the useful information. That's why people have invented and developed unique tools that they get some relevant information. In this paper, the recommender system, one of the effective tools, is used and it helps us to get the useful information that we want by using demographic information to predict new items of interest. The demographic recommender system in this paper computes users' similarity using demographic information, age and gender. So we performed demographic analysis on movie ratings on Internet Movie Database (IMDB) web site that movies are rated by thousands of people, where users submitted a movie rating after they watched a recent popular film. Meanwhile, we can understand that user's ratings, among various determinants of box office, is very essential factor in the study on recommendation of movie. This paper is aimed at analyzing movie average ratings directly given by film viewers, categorizing them into groups by sex and age, investigating the entire group and finding the representative group by examining it with F-test and T-test. This result is used to promote and recommend for the target group only. Therefore, this study is considerably significant as presenting utilization for movie business as well as showing how to analyze demographic information on movie ratings on the web.

• Proceedings of the Korea Information Processing Society Conference
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• 2008.05a
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• pp.40-43
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• 2008

This paper describes a Grid-based context-aware doctor recommender system which recommends appropriate doctors for a patient or user at the right time in the right place. The core of the system is a recommendation mechanism that analyzes a user's demographic profile, user's current context information (i.e., location, time, and weather), and user's position so that doctor information can be ranked according to the match with the preferences of a user. The performance of our architecture is evaluated compare to centralized recommender system.

• Journal of the Korea Society of Computer and Information
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• v.22 no.11
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• pp.97-104
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• 2017

Location prediction has been successfully utilized to provide high quality of location-based services to customers in many applications. In its usual form, the conventional type of location prediction is to predict future locations based on user's past movement history. However, as location prediction needs are expanded into much complicated cases, it becomes necessary quite frequently to make inference on the locations that target user visited in the past. Typical cases include the identification of locations that infectious disease carriers may have visited before, and crime suspects may have dropped by on a certain day at a specific time-band. Therefore, primary goal of this study is to predict locations that users visited in the past. Information used for this purpose include user's demographic information and movement histories. Data mining classifiers such as Bayesian network, neural network, support vector machine, decision tree were adopted to analyze 6868 contextual dataset and compare classifiers' performance. Results show that general Bayesian network is the most robust classifier.

• Journal of the Korean Data and Information Science Society
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• v.17 no.1
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• pp.77-86
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• 2006

This study is to investigate the error of prediction value with related variables from the recommended system and to examine the error of prediction value with related variables. To decrease the error on the collaborative recommended system on user based, this research explored the effects on the prediction related response pair between raters' demographic variables and Pearson's coefficient and sparsity. The result shows comparative analysis between existing error of prediction value and conditioned one.

• The Korean Journal of Health Service Management
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• v.5 no.1
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• pp.1-13
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• 2011

The purpose of this paper was to evaluate performance of information system for one national university hospital in order to identify the factors influencing performance of information system. KPIs were collected for 181 users of information system (41 doctors, 104 nurses, and 11 medical supporting staffs, and 25 administrative staffs) from August 10 to 24, 2010. The results were as follows: Average performance score for input layer was 3.16; average performance score for process layer was 3.35; and average performance score for business layer was 3.57. Scores for input layer was lowest for nurses and scores for process and business layer were lowest for doctors. Results from the path analysis showed that system quality, demographic characteristics, and security significantly influenced management process but these factors except demographic characteristics influenced user satisfaction; and management process also significantly influenced user satisfaction.

• ETRI Journal
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• v.21 no.4
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• pp.83-93
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• 1999

This paper examines the correlations between user characteristics and their perferences for two selected features of Web-based OPAC systems. User characteristics identified in this study were age, gender, educational status, computer skills and OPAC experience. Usability features included interaction styles, character and image on screen, browsing and navigating style, screen layout, and ease of learning, whereas availability features attended to availability of information, quality of information and up-to-date information. Individual variables and features are described, and the correlation between the variables and the features are explored using Pearson's correlation coefficient(r). Although based on a small-scale sample survey, a considerably large number of statistically significant correlations were found between the users' characteristics and the selected evaluation features of interactive Web-based OPACs. From these observations, it seems to be suitable to recommend that system designers should make a more considered appraisal of the users' demographic characteristics in the design of the new generation of OPAC such as in user-tailored interactive Web-based OPAC systems.

• Journal of the Korean Society for Library and Information Science
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• v.48 no.3
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• pp.193-215
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• 2014

This study examines undergraduate nursing students' user satisfaction and affective experience of collaborative information behavior in a group-based learning through survey method to see the relationship between these factors. For this purpose, the affective experiences (positive and negative aspects) of collaborative information search were examined using the PANAS scale through the same questionnaire. Correlation between students' experiences of collaborative information search and affective aspects were examined, and statistical significance using the t-test were also conducted to measure the relationship among students' demographic factors, experiences of collaborative information search and affective aspects. The results revealed that there was a significant correlation between experiences of collaborative information search and affective aspects. Also, there were significant relationships among students' demographic factors, experiences of collaborative information search and affective aspects. In particular, there were signigicant differences in students' overall satisfaction of collaboration and positive aspects between male and female students who experienced collaborative information search. This study found out the possibilities for a follow-up study for affective aspects in collaborative information behavior.