• Asia pacific journal of information systems
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• v.14 no.2
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• pp.131-149
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• 2004

Collaborative filtering is one of popular techniques for personalized recommendation in e-commerce sites. An advantage of collaborative filtering is that the technique can work with sparse evaluation data to predict preference scores of new alternative contents or advertisements. There is, however, no in-depth study about the sparsity effect of customer's evaluation data to the performance of recommendation. In this study, we investigate the sparsity effect and hybrid usages of customers' evaluation data and purchase data using an experiment result. The result of the analysis shows that the performance of recommendation decreases monotonically as the sparsity increases, and also the hybrid usage of two different types of data; customers' evaluation data and purchase data helps to increase the performance of recommendation in sparsity situation.

• Journal of Internet Computing and Services
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• v.8 no.6
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• pp.95-102
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• 2007

Recommender System based on the Collaborative Filtering has a problem of trust of the prediction accuracy because of its problem of sparsity. If the sparsity of a preference value is large, it causes a problem on a process of a choice of neighbors and also lowers the prediction accuracy. In this article, a change of MAE based on the sparsity is studied, groups are classified by sparsity and then, the significant difference among MAEs of classified groups is analyzed. To improve the accuracy of prediction among groups by the problem of sparsity, We studied the improvement of an accurate prediction for recommending system through reducing sparsity by sorting sparsity items, and replacing the average preference among them that has a lot of respondents with the preference evaluation value.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2014.05a
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• pp.413-416
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• 2014

Recently, demand for various leisure sports gradually increases, as people's sense of values changes into leisure-centered one according to the change of given social circumstance and the change of customer needs all over the world. The actual condition is that an interest and participation rate especially in water leports during the summer increases. And needs for various hull design of standardized boat for water leisure increase. Therefore, this paper is intended to develop a recommendation system to design a boat for water leisure by using the collaborative filtering technique in order to make it possible to actively cope with the change of various customer needs for hull design. To this end, emotion relating to kayak design was selected through consumer survey, and emotion was derived by factor analysis and assessment, and then a kayak design layout in the aspect of customer's emotional preference was presented. Besides, an analysis was made according to the elements such as hull, body, and propulsion system of kayak in order to select emotional words according to the kayak design reflecting user's preference, and then a boat model for water leisure in conformance with user's preference was presented.

• Journal of The Korean Association of Information Education
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• v.11 no.3
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• pp.281-288
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• 2007

Collaborative filtering is a method to predict preference items of a user based on the evaluations of items provided by others with similar preferences. Collaborative filtering helps general people make smart decisions in today's information society where information can be easily accumulated and analyzed. We designed, implemented, and evaluated a course recommendation system experimentally. This system can help university students choose courses they prefer to. Firstly, the system needs to collect the course preferences from students and store in a database. Users showing similar preference patterns are considered into similar groups. We use Pearson correlation as a similarity measure. We select K-nearest students to predict the unknown preferences of the student and provide a ranked list of courses based on the course preferences of K-nearest students. We evaluated the accuracy of the recommendation by computing the mean absolute errors of predictions using a survey on the course preferences of students.

• The Journal of the Korea Contents Association
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• v.11 no.5
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• pp.67-74
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• 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 Digital Convergence
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• v.16 no.7
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• pp.243-248
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• 2018

The most important factor for collaborative learning to be effective is the selection of tools that constitute groups. Grouping is to facilitate collaborative learning, learners form groups based on various characteristics. If a group of students fails to form properly due to the selection of the wrong tools, problems can arise where complaints from students can lead to lectures and the effects of learning. In this paper, we have implemented a group of configuration tools that considered improving learning effects and diagnosing bulling tendency. We have proposed a group composition tool that can take into consideration the learning effect and also diagnose the tendency of the bullring by constructing the group according to the teacher's preference by inputting the class preference and the student's grade through the sociometry survey. We expect that the teacher will be able to grasp the students' friendship in advance and cope with the bulling that can happen in the class, as well as the cooperative learning that can lead the class to improve the learning effect.

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

• Journal of Intelligence and Information Systems
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• v.9 no.2
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• pp.51-63
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• 2003

Collaborative filtering is one of the methodologies that are most widely used for recommendation system. It is based on a data matrix of each customer's preferences of products. There could be a lot of missing values in such preference data matrix. This incomplete data is one of the reasons to deteriorate the accuracy of recommendation system. There are several treatments to deal with the incomplete data problem such as case deletion and single imputation. Those approaches are simple and easy to implement but they may provide biased results. Multiple imputation method imputes m values for each missing value. It overcomes flaws of single imputation approaches through considering the uncertainty of missing values. The objective of this paper is to suggest multiple imputation-based collaborative filtering approach for recommendation system to improve the accuracy in prediction performance. The experimental works show that the proposed approach provides better performance than the traditional Collaborative filtering approach, especially in case that there are a lot of missing values in dataset used for recommendation system.

• Proceedings of the CALSEC Conference
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• 2004.02a
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• pp.239-250
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• 2004

The preference of customers change as time goes by. The existing Collaborative Filtering (CF) techniques has no room for including this change yet, although these techniques have been known to be the most successful recommendation technique that has been used in a number of different applications. In this study, we proposed a new methodology for enhancing the quality of recommendation using the customers' dynamic behaviors over time. The proposed methodology is applied to a large department store in Korea, compared to existing CF techniques. Some experiments on the real world data show that the proposed methodology provides higher quality recommendations than other CF techniques, especially better performance on heavy users.

• Journal of Information Technology Services
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• v.6 no.2
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• pp.113-123
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• 2007

In ubiquitous computing environment such as ubiquitous marketplace (u-market), there is a need of providing context-based personalization service while considering the nomadic user preference and corresponding requirements. To do so, the recommendation systems should deal with the tremendous amount of context data. Hence, the purpose of this paper is to propose a novel recommendation method which provides the products-group list of the customers in u-market based on the shopping intention and preferences. We have developed FREPIRS(FREquent Purchased Item-sets Recommendation Service), which makes recommendation listof product-group, not individual product. Collaborative filtering and apriori algorithm are adopted in FREPIRS to build product-group.