• Environmental and Resource Economics Review
• /
• v.21 no.4
• /
• pp.921-945
• /
• 2012

Conditional Logit (CL) model is widely used since its model estimation and interpretation of results of the model is relatively easy, on the other hand, it has the limit of preference heterogeneity of respondents being not fully considered. In this study we used the two models, Mixed Logit (ML) Model and Latent Class Model (LCM) to explain preference heterogeneity of respondents for protection for Boryeong Dam wetland. As a result of the examination for heterogeneity in Boryeong city and six metropolitan areas, we found there was significant difference between two regions. While there was explicit preference heterogeneity within respondents in Boryeong city, we found little heterogeneity within respondents in six metropolitan areas. Thus in the case of six metropolitan areas, CL model can be used for parameter estimation while in the case of Boryeong city, WTP estimates are based on parameter estimates from ML model to reflect the heterogeneity within respondents. Additionally, ML model with interaction and 2-class LCM for respondents in Boryeong city were used to explain the sources of the heterogeneity. The ML model with interaction has advantage of explaining individual unobserved heterogeneity. However The comarison between these two models reflects the fact that LCM provided added information that was not conveyed in the ML model with interaction. Thus, Preference heterogeneity within respondents in this study may be better explained by class level through LCM rather than indiviual level through ML model.

• Journal of Broadcast Engineering
• /
• v.18 no.1
• /
• pp.88-97
• /
• 2013

Collaborative filtering(CF) for the personalized recommendation is a successful and popular method in recommender systems. But the mainly researched and implemented cases focus on dealing with independent items with explicit feedback by users. For the domain of TV program recommendation in VOD service platform, we need to consider the unique characteristic and constraints of the domain. In this paper, we studied on the way to convert the viewing history of each TV program episodes to the TV program preference by considering the series structure of TV program. The former is implicit for personalized preference, but the latter tells quite explicitly about the persistent preference. Collaborative filtering is done by the unit of series while data gathering and final recommendation is done by the unit of episodes. As a result, we modified CF to make it more suitable for the domain of TV program VOD recommendation. Our experimental study shows that it is more precise in performance, yet more compact in calculation compared to the plain CF approaches. It can be combined with other existing CF techniques as an algorithm module.

• Language and Information
• /
• v.13 no.2
• /
• pp.1-27
• /
• 2009

This paper examines the (a)symmetries in the realization of focused subjects and objects in Korean. Through rating experiments, we demonstrate that native speakers' judgments of acceptability of sentences containing case-marked or case-ellipsed subjects and objects are sensitive to the contrastiveness strength and the discourse accessibility of focused arguments. However, our experiments also show that focused subjects exhibit stronger preference for explicit case marking over case ellipsis and that contrastiveness strength and discourse accessibility have weaker influence on the case marking and ellipsis of focused subjects compared to focused objects. We propose an account of variable case marking that is capable of subsuming both the similarities and differences between focused subjects and objects under the universal theory of markedness. In particular, it is shown that the similarities between focused subjects and objects are predicted by the proposed account based on the contrastiveness strength and the discourse accessibility of focused arguments and that the differences between focused subjects and objects follow naturally from the relative markedness of focus as subjects.

• Structural Engineering and Mechanics
• /
• v.70 no.2
• /
• pp.153-168
• /
• 2019

In this paper, energy absorption characteristics of circular windowed tubes with different section shapes (circular, ellipse, square, hexagon, polygon and pentagon) are investigated numerically and experimentally. The tube possesses the same material, thickness, height, volume and average cross sectional area which are subjected under axial and oblique quasi-static loading conditions. Numerical model was constructed with FE code ABAQUS/Explicit, the obtained outcome of simulation is in good matching with the experimental data. The energy absorbed, specific energy absorption, crash force efficiency, peak and mean loads along with the collapse modes with their initiation point of simple and windowed tubes were evaluated. The technique for order of preference by similarity ideal solution (TOPSIS) approach was employed for assessing their overall crushing performances. The obtained results confirm that efficacy of crash force indicators have improved by introducing windows and tubes with pentagonal and circular windows achieves the maximum ranking about 0.528 and 0.517, it clearly reveals the above are best window shapes.

• Information Systems Review
• /
• v.14 no.3
• /
• pp.115-129
• /
• 2012

A matchmaking system is a type of recommender systems that provides a set of dating partners suitable for the user by online. Many matchmaking systems, which are widely used these days, require users to specify their preferences with regards to ideal dating partners based on criteria such as age, job and salary. However, some users are not aware of their exact preferences, or are reluctant to reveal this information even if they do know. Also, users' selection standards are not fixed and can change according to circumstances. This paper suggests a new matchmaking system called Decision Tree based Matchmaking System (DTMS) that automatically adjusts the stated standards of a user by analyzing the characteristics of the people the user chose to contact. AMMS provides recommendations for new users on the basis of their explicit preferences. However, as a user's behavioral records are accumulated, it begins to analyze their hidden implicit preferences using a decision tree technique. Subsequently, DTMS reflects these implicit preferences in proportion to their predictive accuracy. The DTMS is regularly updated when a user's data size increases by a set amount. This paper suggests an architecture for the DTMS and presents the results of the implementation of a prototype.

• The Journal of the Korea Contents Association
• /
• v.11 no.5
• /
• pp.67-74
• /
• 2011

Collaborative recommendation is one of the most widely used methods of automated product recommendation in e-Commerce. For analyzing the customer's preference, traditional explicit ratings are less desirable than implicit ratings because it may impose an additional burden to the customers of e-commerce companies which deals with a number of products. Cardinal scales generally used for representing the preference intensity also ineffective owing to its increasing estimation errors. In this paper, we propose a new way of constructing the ordinal scale-based customer profile for collaborative recommendation. A Web usage mining technique and lexicographic consensus are employed. An experiment shows that the proposed method performs better than existing CF methodologies.

• Journal of Fashion Business
• /
• v.19 no.1
• /
• pp.122-140
• /
• 2015

The research is committed to inquire about the attributes of color schemes and their image, and the results are as follows : One, the preference of ranges of neon colors was explicit, and the frequency of use of neon colors distinctively diverged season by season. Two, it was observed that, with neon colors, an achromatic color scheme was a more preferred arrangement. As for chromatic colors, neutral and mid-tone natural colors were more favored since they did not tarnish the properties of neon colors and, yet, more effective exhibiting images in diversity and variety. Three, the neon color fashion generally displayed a dual image: its original classification embellished with neon colors rendering the image of powerful and futuristic sensation. Having been around since the early 2000's, the frequency and range of use of neon colors have been increasing rapidly mostly by the sports, leisure and related industries. Regardless of the fact, neon colors will be rediscovered with a variety of color schemes and expand their application.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2021.10a
• /
• pp.189-192
• /
• 2021

YouTube, the world's largest video sharing platform, is loved by many users in that it provides numerous videos and makes it easy to get helpful information. However, the ratio of like/hate for each video varies according to the subject or upload time, even though they are in the same channel; thus, previous studies try to understand the reason by inspecting some numerical statistics such as the ratio and view count. They can help know how each video is preferred, but there is an explicit limitation to identifying the cause of such preference. Therefore, this study aims to determine the reason that affects the preference through matching between topic words extracted from comments in each video and non-sympathetic types defined in advance. Among the top 10 channels in the field of 'pets' and 'cooking', where outliers occur a lot, the top 10 videos (the threshold of pet: 4.000, the threshold of cooking: 0.723) with the highest ratio were selected. 11,110 comments collected totally, and topics were extracted and matched with non-sympathetic types. The experimental results confirmed that it is possible to predict whether the rate of like/hate would be high or which non-sympathetic type would be by analyzing the comments.

• Journal of the Korea Society of Computer and Information
• /
• v.17 no.6
• /
• pp.163-172
• /
• 2012

Collaborative filtering which is used explicit method in a existing recommedation system, 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. This paper proposes the personalized recommendation system using RFM method and k-means clustering in u-commerce which is required by real time accessablity and agility. In this paper, using a implicit method which is is not used complicated query processing of the request and the response for rating, it is necessary for us to keep the analysis of RFM method and k-means clustering to be able to reflect attributes of the item in order to find the items with high purchasablity. The proposed makes the task of clustering to apply the variable of featured vector for the customer's information and calculating of the preference by each item category based on purchase history data, is able to recommend the items with efficiency. To estimate the performance, the proposed system is compared with existing system. As a result, it can be improved and evaluated according to the criteria of logicality through the experiment with dataset, collected in a cosmetic internet shopping mall.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.31 no.7B
• /
• pp.619-631
• /
• 2006

With the advent of ubiquitous computing environments, it has become increasingly important for applications to take full advantage of context information, such as the user's location, to offer greater services to the user without any explicit request. In this paper, we propose context-aware active services on the basis of CAMUS (Context-Aware Middleware for URC Systems). CAMUS is a middleware for providing context-aware applications with development and execution methodology. Accordingly, the applications developed by CAMUS respond in a timely fashion to contexts. To evaluate, we apply proposed active services to TV application domain. Therefore, we implement and experiment the TV contents recommendation service agent, control service agent and TV task based on CAMUS. The context-aware TV task is to recommend programs and control of TV according to user preference, location and voice commands.