• Journal of information and communication convergence engineering
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• v.17 no.2
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• pp.135-141
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• 2019

Collaborative filtering algorithms often encounter data sparsity issues. To overcome this issue, auxiliary information of relevant items is analyzed and an item attribute matrix is derived. In this study, we combine the user-item attribute preference with the traditional similarity calculation method to develop an improved similarity calculation approach and use weights to control the importance of these two elements. A collaborative filtering algorithm based on user-item attribute preference is proposed. The experimental results show that the performance of the recommender system is the most optimal when the weight of traditional similarity is equal to that of user-item attribute preference similarity. Although the rating-matrix is sparse, better recommendation results can be obtained by adding a suitable proportion of user-item attribute preference similarity. Moreover, the mean absolute error of the proposed approach is less than that of two traditional collaborative filtering algorithms.

• Journal of Digital Convergence
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• v.6 no.4
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• pp.177-183
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• 2008

This paper aims to offer a solution based on intelligent document classification to create a user-centric information retrieval system allowing user-centric linguistic expression. So, structures expressing user intention and fine document classifying process using EBL, similarity, knowledge base, user intention, are proposed. To overcome the problem requiring huge and exact semantic information, a hybrid process is designed integrating keyword, thesaurus, probability and user intention information. User intention tree hierarchy is build and a method of extracting group intention between key words and user intentions is proposed. These structures and processes are implemented in HDCI(Hybrid Document Classification with Intention) system. HDCI consists of analyzing user intention and classifying web documents stages. Classifying stage is composed of knowledge base process, similarity process and hybrid coordinating process. With the help of user intention related structures and hybrid coordinating process, HDCI can efficiently categorize web documents in according to user's complex linguistic expression with small priori information.

• The Journal of Korean Institute of Information Technology
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• v.17 no.12
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• pp.29-38
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• 2019

A path prediction method using lifelog requires a large amount of training data for accurate path prediction, and the path prediction performance is degraded when the training data is insufficient. The lack of training data can be solved using data of other users having similar user movement patterns. Therefore, this paper proposes a path prediction algorithm based on user similarity. The proposed algorithm learns the path in a triple grid pattern and measures the similarity between users using the cosine similarity technique. Then, it predicts the path with applying measured similarity to the learned model. For the evaluation, we measure and compare the path prediction accuracy of proposed method with the existing algorithms. As a result, the proposed method has 66.6% accuracy, and it is evaluated that its accuracy is 1.8% higher than other methods.

• The Journal of Korean Association of Computer Education
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• v.21 no.5
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• pp.61-68
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• 2018

Collaborative filtering is a fundamental technique implemented in many commercial recommender systems and provides a successful service to online users. This technique recommends items by referring to other users who have similar rating records to the current user. Hence, similarity measures critically affect the system performance. This study addresses problems of previous similarity measures and suggests a new similarity measure. The proposed measure reflects the subjectivity or vagueness of user ratings and the users' rating behavior by using fuzzy logic. We conduct experimental studies for performance evaluation, whose results show that the proposed measure demonstrates outstanding performance improvements in terms of prediction accuracy and recommendation accuracy.

• International journal of advanced smart convergence
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• v.9 no.3
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• pp.42-48
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• 2020

These days people are overwhelmed by information on the Internet thus searching for useful information becomes burdensome, often failing to acquire some in a reasonable time. Recommender systems are indispensable to fulfill such user needs through many practical commercial sites. This study proposes a novel similarity measure for user-based collaborative filtering which is a most popular technique for recommender systems. Compared to existing similarity measures, the main advantages of the suggested measure are that it takes all the ratings given by users into account for computing similarity, thus relieving the inherent data sparsity problem and that it reflects the uncertainty or vagueness of user ratings through fuzzy logic. Performance of the proposed measure is examined by conducting extensive experiments. It is found that it demonstrates superiority over previous relevant measures in terms of major quality metrics.

• Journal of the Korea Society of Computer and Information
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• v.24 no.9
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• pp.77-82
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• 2019

Collaborative filtering is a popular technique for recommender systems and used in many practical commercial systems. Its basic principle is select similar neighbors of a current user and from their past preference information on items the system makes recommendations for the current user. One of the major problems inherent in this type of system is data sparsity of ratings. This is mainly caused from the underlying similarity measures which produce neighbors based on the ratings records. This paper handles this problem and suggests a new similarity measure. The proposed method takes users rating patterns into account for computing similarity, without just relying on the commonly rated items as in previous measures. Performance experiments of various existing measures are conducted and their performance is compared in terms of major performance metrics. As a result, the proposed measure reveals better or comparable achievements in all the metrics considered.

• Journal of the Korea Society of Computer and Information
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• v.20 no.11
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• pp.25-30
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• 2015

The enormous increase of data with the development of the information technology make internet users to be hard to find suitable information tailored to their needs. In the face of changing environment, the information filtering method, which provide sorted-out information to users, is becoming important. The data on the internet exists as various type. However, similarity calculation algorithm frequently used in existing collaborative filtering method is tend to be suitable to the numeric data. In addition, in the case of the categorical data, it shows the extreme similarity like Boolean Algebra. In this paper, We get the similarity in SNS user's information which consist of the mixed data using the Gower's similarity coefficient. And we suggest a method that is softer than radical expression such as 0 or 1 in categorical data. The clustering method using this algorithm can be utilized in SNS or various recommendation system.

• 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.14 no.5
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• pp.97-104
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• 2011

Collaborative filtering is a methodology to recommend websites by obtaining data and opinions from the other users with similar tastes. During the past few years, this method has been used in various fields such as books, food, and movies in e-commerce systems. This study addresses the computation of similarity between users to determine items to be recommended in collaborative filtering systems. Previous studies measured similarity between users by treating each user's ratings independently without considering the distribution of the user's ratings. In contrast, this study measures similarity by utilizing position and rank information of each rating in the range of the user's ratings. The result of the experiments on the real datasets demonstrated that the proposed method improves the mean absolute error significantly, compared to the previous methods, especially when the predetermined range of ratings is large.

• International Journal of Advanced Culture Technology
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• v.10 no.2
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• pp.240-245
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• 2022

Because of the importance of the information, encryption algorithms are heavily used. Raw data is encrypted and secure, but problems arise when the key for decryption is exposed. In particular, large-scale Internet sites such as Facebook and Amazon suffer serious damage when user data is exposed. Recently, research into a new fourth-generation encryption technology that can protect user-related data without the use of a key required for encryption is attracting attention. Also, data clustering technology using encryption is attracting attention. In this paper, we try to reduce key exposure by using homomorphic encryption. In addition, we want to maintain privacy through similarity measurement. Additionally, holistic similarity measurements are time-consuming and expensive as the data size and scope increases. Therefore, Min-Hash has been studied to efficiently estimate the similarity between two signatures Methods of measuring similarity that have been studied in the past are time-consuming and expensive as the size and area of data increases. However, Min-Hash allowed us to efficiently infer the similarity between the two sets. Min-Hash is widely used for anti-plagiarism, graph and image analysis, and genetic analysis. Therefore, this paper reports privacy using homomorphic encryption and presents a model for efficient similarity measurement using Min-Hash.