• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.11
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• pp.2602-2608
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• 2013

User-based and item-based method have been developed as the solutions of the movie recommendation problem. However, these methods are faced with the sparsity problem and the problem of not reflecting user's rating respectively. In order to solve these problems, there is a research on the combination of the two methods using the concept of similarity. In reality, it is not free from the problem of sparsity, since it has a lot of parameters to be calculated. In this study, we propose a recommendation method using rating difference between items in order to complement this problem. This method is relatively free from the problem of sparsity, since it has less parameters to be calculated. And it can get more accurate results by reflecting the users rating to calculate the parameters. In experiments for the proposed method, the initial error is large, but the performance has been quickly stabilized after. In addition, it showed a 0.0538 lower average error compared to the existing method using similarity.

• Journal of the Korea Society of Computer and Information
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• v.16 no.9
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• pp.19-26
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• 2011

Users want to receive customized service using users' personal device. To fulfill this requirement, the mobile device has to support a lot of functions. However, the mobile device has limitations such as tiny display screens. To solve this limitation problem and provide customized service to users, this paper proposes the environment to provide services by sharing resources and the method to recommend user-suitable resources among sharable resources. For the resource recommendation, This paper analyzes user's behavior pattern from usage history and proposes the method for recommending customized resources. This paper also shows that the approach is reasonable one for resource recommendation through the satisfaction evaluation.

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

• The Journal of Korean Association of Computer Education
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• v.19 no.4
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• pp.59-66
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• 2016

It has been studied to reflect the number of co-rated items for solving data sparsity problem in collaborative filtering systems. A well-known method of Jaccard index allowed performance improvement, when combined with previous similarity measures. However, the degree of performance improvement when combined with existing similarity measures in various data environments are seldom analyzed, which is the objective of this study. Jaccard index as a sole similarity measure yielded much higher prediction quality than traditional measures and very high recommendation quality in a sparse dataset. In general, previous similarity measures combined with Jaccard index improved performance regardless of dataset characteristics. Especially, cosine similarity achieved the highest improvement in sparse datasets, while similarity of Mean Squared Difference degraded prediction quality in denser sets. Therefore, one needs to consider characteristics of data environment and similarity measures before combining Jaccard index for similarity use.

• Journal of KIISE
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• v.43 no.10
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• pp.1144-1153
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• 2016

Group recommendation analyzes the characteristics and tendency of a group rather than an individual and provides relevant information for the members of the group. Existing group recommendation methods merely consider the average and frequency of a preference. However, if the users' preferences have large deviations, it is difficult to provide satisfactory results for all users in the group, although the average and frequency values are high. To solve these problems, we propose a method that considers not only the average of a preference but also the deviation. The proposed method provides recommendations with high average values and low deviations for the preference, so it reflects the tendency of all group members better than existing group recommendation methods. Through a comparative experiment, we prove that the proposed method has better performance than existing methods, and verify that it has high performance in groups with a large number of members as well as in small groups.

• Journal of Internet Computing and Services
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• v.14 no.5
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• pp.59-67
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• 2013

In this paper, we propose the LAR_CF, latent attribute rating-based collaborative filtering, that is robust to data sparsity problem which is one of traditional problems caused of decreasing rating prediction accuracy. As compared with that existing collaborative filtering method uses a preference rating rated by users as feature vector to calculate similarity between objects, the proposed method improves data sparsity problem using unique attributes of two target objects with existing explicit preference. We consider MovieLens 100k dataset and its item attributes to evaluate the LAR_CF. As a result of artificial data sparsity and full-rating experiments, we confirmed that rating prediction accuracy can be improved rating prediction accuracy in data sparsity condition by the LAR_CF.

• 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 The Korean Association of Information Education
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• v.17 no.4
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• pp.375-382
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• 2013

Collaborative filtering has been most widely used in commercial sites to recommend items based on the history of user preferences for items. The basic idea behind this method is to find similar users whose ratings for items are incorporated to make recommendations for new items. Hence, similarity calculation is most critical in recommendation performance. This paper presents a new similarity measure that takes each rating of a user relatively to his own ratings. Extensive experiments revealed that the proposed measure is more reliable than the classic measures in that it significantly decreases generation of extreme similarity values and its performance improves when consulting neighbors with high similarites only. In particular, the results show that the proposed measure is superior to the classic ones for datasets with large rating scales.

• The Journal of Information Systems
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• v.21 no.4
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• pp.155-183
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• 2012

Means-Ends Network model was used to identify factors of means objective(means supplied by vendor) and fundamental objectives(purchasing motivations) for purchasing decision-making structure and dimensions of customer values on purchasers of internet shopping mall in Korea and China. In Means-Ends Network 6 factors(shopping travel, shipping assurance, vendor trust, online payment, product choice, and recommender systems) were found as a means objectives and 3 factors(shopping convenience, internet environment, customer support) as a fundamental objectives of shopping. However the results of hypotheses test for Means-Ends Network show some important differences between two countries. Something important to notice here is that Chinese customers shopping in China recognize shipping assurance factor and vendor trust factor as important factors satisfying all fundamental objectives unlike as in the case of our country. As these two factors are attribution factors responsible to the sellers, it is identified that customers do not trust the sellers and sellers have not met the expectations of customers. Therefore, these results show that the seller efforts assuring the reliability of the seller themselves, such as conducting its own compensation scheme are more important rather than the establishment of the guarantee institution to guarantee reliability and delivery assurance of sellers and implementation of legal and institutional apparatus such as the settlement of e-commerce licence system. Though this study presents such an important marketing implications, it can be pointed out that the limits are this research was done on the general Internet shopping malls without considering the Internet shopping mall types of diversity, the survey was designed around the student samples for convenience of the investigation because it was an international survey and the collected data has been limited to the western coast cities, such as China's Beijing, Shanghai, and Dalian.

• Journal of KIISE:Software and Applications
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• v.32 no.9
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• pp.901-911
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• 2005

With the development of e-commerce and the proliferation of easily accessible information, information filtering has become a popular technique to prune large information spaces so that users are directed toward those items that best meet their needs and preferences. While many collaborative filtering systems have succeeded in capturing the similarities among users or items based on ratings to provide good recommendations, there are still some challenges for them to be more efficient, especially the user bias problem, non-transitive association problem and cold start problem. Those three problems impede us to capture more accurate similarities among users or items. In this paper, we provide probabilistic model approaches for UCHM and ICHM which are suggested to solve the addressed problems in hopes of achieving better performance. In this probabilistic model, objects (users or items) are classified into groups and predictions are made for users considering the Gaussian distribution of user ratings. Experiments on a real-word data set illustrate that our proposed approach is comparable with others.