• KIPS Transactions on Computer and Communication Systems
• /
• v.4 no.9
• /
• pp.289-296
• /
• 2015

The explosive growth of information has been difficult for users to get an appropriate information in time. The various ways of new services to solve problems has been provided. As customized service is being magnified, the personalized recommendation system has been important issue. Collaborative filtering system in the recommendation system is widely used, and it is the most successful process in the recommendation system. As the recommendation is based on customers' profile, there can be sparsity and cold-start problems. In this paper, we propose personalized movie recommendation system using collaborative filtering techniques and context-based techniques. The context-based technique is the recommendation method that considers user's environment in term of time, emotion and location, and it can reflect user's preferences depending on the various environments. In order to utilize the context-based technique, this paper uses the human emotion, and uses movie reviews which are effective way to identify subjective individual information. In this paper, this proposed method shows outperforming existing collaborative filtering methods.

• Journal of Korean Society of Coastal and Ocean Engineers
• /
• v.5 no.4
• /
• pp.406-413
• /
• 1993

Among 2-dimensional coastal hydrodynamic finite element models time stepping ADCIRC and STEPM. and harmonic FUNDY and TEA models were compared in order to find out their characteristics and analyze ernr. General feasibility and capability of models were studied by comparing model results with an analytical solution on some reference points and L$_2$norm error in quarter annular domain where analytical solution can be obtained. According to these tests harmonic models FUNDY and TEA were nearly coinciding with analytical solutions and gave better results than time stepping models. STEPM was at least 5 times better than ADCIRC in L$_2$norm error test and it was 7 times worse than harmonic models. It was expected and concluded that these errors might come from phase lag due to cold start condition and nonlinear effect in basic equations of time stepping models.

• Transactions of the Korean Society of Automotive Engineers
• /
• v.20 no.4
• /
• pp.25-32
• /
• 2012

Cold start driving cycles exhibit an increases in friction losses due to the low temperatures of metal components and media compared to the normal operating engine conditions. These friction losses are adversely affected to fuel economy. Therefore, in recent years, various techniques for the improvement of fuel economy at cold start driving cycles have been introduced. The main techniques are the upward control of coolant temperature and the fast warm-up techniques. In particular, the fast warm-up techniques are implemented with the coolant flow-controlled water pump and the WHRS (waste heat recovery system). This paper deals with an effect of fast ATF (automatic transmission fluid) warm-up on fuel economy using a recovery system of EGR gas waste heat in a diesel engine. On a conventional diesel engine, two ATF coolers have been connected in series, i.e., an air-cooled ATF cooler is placed in front of the condenser of air conditioning system and a water-cooled one is embedded into the radiator header. However, the new system consists of only a water-cooled heat exchanger that has been changed into the integrated structure with an EGR cooler to have the engine coolant directly from the EGR cooler. The ATF cooler becomes the ATF warmer and cooler, i.e., it plays a role of an ATF warmer if the temperature of ATF is lower than that of coolant, and plays a role of an ATF cooler otherwise. Chassis dynamometer experiments demonstrated the fuel economy improvement of over 2.5% with rapid increase in the ATF temperature.

• Journal of Energy Engineering
• /
• v.18 no.1
• /
• pp.69-73
• /
• 2009

The regulations for exhaust emission from vehicles have become much more stringent in recent years. These more stringent regulations require vehicle manufacturers to develop alternative fuels that reduce exhaust emission. This research is to analyze the characteristics of exhaust gas emission of same level vehicles that use gasoline, diesel, and LPG fuels. As for the test mode, we used the CVS-75 mode, which is the driving mode of the current domestic and North American emissions. The characteristics of the exhaust gas emitted under this driving condition was studied. We examined the emissions of THC, CO, and NOx of vehicles that use gasoline, diesel, and LPG fuels. As a result, vehicle exhaust gas emissions increased 9.8 % for vehicles using gasoline and it decreased 12.2 % for diesel-powered vehicles compared to vehicles using LPG fuel. Using gasoline and LPG fuel in the CVS-mode, over 80 % of THC and CO emission was produced for the cold start Phase 1.

• Journal of KIISE
• /
• v.43 no.11
• /
• pp.1188-1200
• /
• 2016

Recommendation systems play a significant role in providing personalized information to users, with enhanced satisfaction and reduced information overload. Since the mid-1990s, many studies have been conducted on recommendation systems, but few have examined the recommendations of information from people in the online social networking environment. In this paper, we present a hybrid recommendation method that combines both the traditional system of content-based techniques to improve specialization, and the recently developed system of social network-based techniques to best overcome a few limitations of the traditional techniques, such as the cold-start problem. By suggesting a state-of-the-art method, this research will help users in online social networks view more personalized information with less effort than before.

• Journal of the Korea Society of Computer and Information
• /
• v.24 no.12
• /
• pp.93-100
• /
• 2019

Similarity computation is very crucial to performance of memory-based collaborative filtering systems. These systems make use of user ratings to recommend products to customers in online commercial sites. For better recommendation, most similar users to the active user need to be selected for their references. There have been numerous similarity measures developed in literature, most of which suffer from data sparsity or cold start problems. This paper intends to extract preference information as much as possible from user ratings to compute more reliable similarity even in a sparse data condition, as compared to previous similarity measures. We propose a new similarity measure which relies not only on user ratings but also on movie genre information provided by the dataset. Performance experiments of the proposed measure and previous relevant measures are conducted to investigate their performance. As a result, it is found that the proposed measure yields better or comparable achievements in terms of major performance metrics.

• Journal of KIISE:Software and Applications
• /
• v.32 no.6
• /
• pp.542-553
• /
• 2005

Recommendation systems analyze user preferences and recommend items to a user by predicting the user's preference for those items. Among various kinds of recommendation methods, collaborative filtering(CF) has been widely used and successfully applied to practical applications. However, collaborative filtering has two inherent problems: data sparseness and the cold-start problems. If there are few known preferences for a user, it is difficult to find many similar users, and therefore the performance of recommendation is degraded. This problem is more serious when a new user is first using the system. In this paper we propose a method of integrating additional feature information of users and items into CF to overcome the difficulties caused by sparseness and improve the accuracy of recommendation. In our method, we first fill in unknown preference values by using the probability distribution of feature values, then generate the top-N recommendations by applying collaborative filtering on the modified data. We call this method of filling unknown preference values as data blurring. Several experimental results that show the effectiveness of the proposed method are also presented.

• Journal of Digital Contents Society
• /
• v.18 no.4
• /
• pp.707-712
• /
• 2017

As the amount of data increases exponentially, the recommender system is attracting interest in various industries such as movies, books, and music, and is being studied. The recommendation system aims to propose an appropriate item to the user based on the user's past preference and click stream. Typical examples include Netflix's movie recommendation system and Amazon's book recommendation system. Previous studies can be categorized into three types: collaborative filtering, content-based recommendation, and hybrid recommendation. However, existing recommendation systems have disadvantages such as sparsity, cold start, and scalability problems. To improve these shortcomings and to develop a more accurate recommendation system, we have designed a recommendation system as a factorization machine using actual online product purchase data.

• Journal of KIISE:Software and Applications
• /
• v.31 no.12
• /
• pp.1672-1683
• /
• 2004

Recommender system is a kind of web intelligence techniques to make a daily information filtering for people. Researchers have developed collaborative recommenders (social recommenders), content-based recommenders, and some hybrid systems. In this paper, we introduce a new hybrid recommender method - ICHM where clustering techniques have been applied to the item-based collaborative filtering framework. It provides a way to integrate the content information into the collaborative filtering, which contributes to not only reducing the sparsity of data set but also solving the cold start problem. Extensive experiments have been conducted on MovieLense data to analyze the characteristics of our technique. The results show that our approach contributes to the improvement of prediction quality of the item-based collaborative filtering, especially for the cold start problem.

• Journal of KIISE:Software and Applications
• /
• v.33 no.11
• /
• pp.953-964
• /
• 2006

Due to the usefulness of the collaborative filtering, it has been widely used in both the research and commercial field. However, there are still some challenges for it to be more efficient, especially the scalability problem, the sparsity problem and the cold start problem. In this paper. we address these problems and provide a novel distributed approach based on agents collaboration for the problems. We have tried to solve the scalability problem by making each agent save its users ratings and broadcast them to the users friends so that only friends ratings and his own ratings are kept in an agents local database. To reduce quality degradation of recommendation caused by the lack of rating data, we introduce a method using friends opinions instead of real rating data when they are not available. We also suggest a collaborative filtering algorithm based on user profile to provide new users with recommendation service. Experiments show that our suggested approach is helpful to the new user problem as well as is more scalable than traditional centralized CF filtering systems and alleviate the sparsity problem.