• Journal of Biomedical Engineering Research
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• v.35 no.3
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• pp.68-74
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• 2014

Recent research on mild cognitive impairment (MCI) has shown that cognitive and memory decline in this disease is accompanied by disruptive changes in the brain functional network. However, there have been no graph-theoretical studies using $^{11}C$-PIB PET data of the Alzheimer's Disease or mild cognitive impairment. In this study, we acquired $^{18}F$-FDG PET and $^{11}C$-PIB PET images of twenty-four normal aging control participants and thirty individuals with MCI from ADNI (Alzheimer's Disease Neuroimaging Initiative) database. Brain networks were constructed by thresholding binary correlation matrices using graph theoretical approaches. Both normal control and MCI group showed small-world property in $^{11}C$-PIB PET images as well as $^{18}F$-FDG PET images. $^{11}C$-PIB PET images showed significant difference between NC (normal control) and MCI over large range of sparsity values. This result will enable us to further analyze the brain using established graph-theoretical approaches for $^{11}C$-PIB PET images.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.5
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• pp.1708-1727
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• 2021

In the advent of the twenty-first century, human beings began to closely interact with technology. Today, technology is developing, and as a result, the world wide web (www) has a very important place on the Internet and the significant task is fulfilled by Web services. A lot of Web services are available on the Internet and, therefore, it is difficult to find matching Web services among the available Web services. The recommendation systems can help in fixing this problem. In this paper, our observation was based on the recommended method such as the collaborative filtering (CF) technique which faces some failure from the data sparsity and the cold-start problems. To overcome these problems, we first applied an ontology-based clustering and then the k-nearest neighbor (KNN) algorithm for each separate cluster group that effectively increased the data density using the past user interests. Then, user ratings were predicted based on the model-based approach, such as singular value decomposition (SVD) and the predictions used for the recommendation. The evaluation results showed that our proposed approach has a less prediction error rate with high accuracy after analyzing the existing recommendation methods.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.2
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• pp.142-149
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• 2004

It is important to retrieve exact information coinciding with user's need from lots of Usenet news and filter desired information quickly. Differently from email system, we must previously register our interesting news group if we want to get the news information. However, it is not easy for a novice to decide which news group is relevant to his or her interests. In this work, we present a service classifying user preferred news groups among various news groups by the use of Kohonen network. We first extract candidate terms from example documents and then choose a number of representative keywords to be used in Kohonen network from them through fuzzy inference. From the observation of training patterns, we could find the sparsity problem that lots of keywords in training patterns are empty. Thus, a new method to train neural network through reduction of unnecessary dimensions by the statistical coefficient of determination is proposed in this paper. Experimental results show that the proposed method is superior to the method using every dimension in terms of cluster overlap defined by using within cluster distance and between cluster distance.

• Journal of KIISE:Software and Applications
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• v.32 no.7
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• pp.581-591
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• 2005

Collaborative filtering systems have problems involving sparsity and the provision of recommendations by making correlations between only two users' preferences. These systems recommend items based only on the preferences without taking in to account the contents of the items. As a result, the accuracy of recommendations depends on the data from user-rated items. When users rate items, it can be expected that not all users ran do so earnestly. This brings down the accuracy of recommendations. This paper proposes a collaborative recommendation method for extracting typical group preferences using user-item matrix optimization and user profiles in collaborative tittering systems. The method excludes unproven users by using entropy based on data from user-rated items and groups users into clusters after generating user profiles, and then extracts typical group preferences. The proposed method generates collaborative user profiles by using association word mining to reflect contents as well as preferences of items and groups users into clusters based on the profiles by using the vector space model and the K-means algorithm. To compensate for the shortcoming of providing recommendations using correlations between only two user preferences, the proposed method extracts typical preferences of groups using the entropy theory The typical preferences are extracted by combining user entropies with item preferences. The recommender system using typical group preferences solves the problem caused by recommendations based on preferences rated incorrectly by users and reduces time for retrieving the most similar users in groups.

• Journal of Intelligence and Information Systems
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• v.20 no.2
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• pp.137-148
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• 2014

Recommender system has become one of the most important technologies in e-commerce in these days. The ultimate reason to shop online, for many consumers, is to reduce the efforts for information search and purchase. Recommender system is a key technology to serve these needs. Many of the past studies about recommender systems have been devoted to developing and improving recommendation algorithms and collaborative filtering (CF) is known to be the most successful one. Despite its success, however, CF has several shortcomings such as cold-start, sparsity, gray sheep problems. In order to be able to generate recommendations, ordinary CF algorithms require evaluations or preference information directly from users. For new users who do not have any evaluations or preference information, therefore, CF cannot come up with recommendations (Cold-star problem). As the numbers of products and customers increase, the scale of the data increases exponentially and most of the data cells are empty. This sparse dataset makes computation for recommendation extremely hard (Sparsity problem). Since CF is based on the assumption that there are groups of users sharing common preferences or tastes, CF becomes inaccurate if there are many users with rare and unique tastes (Gray sheep problem). This study proposes a new algorithm that utilizes Social Network Analysis (SNA) techniques to resolve the gray sheep problem. We utilize 'degree centrality' in SNA to identify users with unique preferences (gray sheep). Degree centrality in SNA refers to the number of direct links to and from a node. In a network of users who are connected through common preferences or tastes, those with unique tastes have fewer links to other users (nodes) and they are isolated from other users. Therefore, gray sheep can be identified by calculating degree centrality of each node. We divide the dataset into two, gray sheep and others, based on the degree centrality of the users. Then, different similarity measures and recommendation methods are applied to these two datasets. More detail algorithm is as follows: Step 1: Convert the initial data which is a two-mode network (user to item) into an one-mode network (user to user). Step 2: Calculate degree centrality of each node and separate those nodes having degree centrality values lower than the pre-set threshold. The threshold value is determined by simulations such that the accuracy of CF for the remaining dataset is maximized. Step 3: Ordinary CF algorithm is applied to the remaining dataset. Step 4: Since the separated dataset consist of users with unique tastes, an ordinary CF algorithm cannot generate recommendations for them. A 'popular item' method is used to generate recommendations for these users. The F measures of the two datasets are weighted by the numbers of nodes and summed to be used as the final performance metric. In order to test performance improvement by this new algorithm, an empirical study was conducted using a publically available dataset - the MovieLens data by GroupLens research team. We used 100,000 evaluations by 943 users on 1,682 movies. The proposed algorithm was compared with an ordinary CF algorithm utilizing 'Best-N-neighbors' and 'Cosine' similarity method. The empirical results show that F measure was improved about 11% on average when the proposed algorithm was used