• Title/Summary/Keyword: Neighbor Centrality

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Enhancing Predictive Accuracy of Collaborative Filtering Algorithms using the Network Analysis of Trust Relationship among Users (사용자 간 신뢰관계 네트워크 분석을 활용한 협업 필터링 알고리즘의 예측 정확도 개선)

  • Choi, Seulbi;Kwahk, Kee-Young;Ahn, Hyunchul
    • Journal of Intelligence and Information Systems
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    • v.22 no.3
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    • pp.113-127
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    • 2016
  • Among the techniques for recommendation, collaborative filtering (CF) is commonly recognized to be the most effective for implementing recommender systems. Until now, CF has been popularly studied and adopted in both academic and real-world applications. The basic idea of CF is to create recommendation results by finding correlations between users of a recommendation system. CF system compares users based on how similar they are, and recommend products to users by using other like-minded people's results of evaluation for each product. Thus, it is very important to compute evaluation similarities among users in CF because the recommendation quality depends on it. Typical CF uses user's explicit numeric ratings of items (i.e. quantitative information) when computing the similarities among users in CF. In other words, user's numeric ratings have been a sole source of user preference information in traditional CF. However, user ratings are unable to fully reflect user's actual preferences from time to time. According to several studies, users may more actively accommodate recommendation of reliable others when purchasing goods. Thus, trust relationship can be regarded as the informative source for identifying user's preference with accuracy. Under this background, we propose a new hybrid recommender system that fuses CF and social network analysis (SNA). The proposed system adopts the recommendation algorithm that additionally reflect the result analyzed by SNA. In detail, our proposed system is based on conventional memory-based CF, but it is designed to use both user's numeric ratings and trust relationship information between users when calculating user similarities. For this, our system creates and uses not only user-item rating matrix, but also user-to-user trust network. As the methods for calculating user similarity between users, we proposed two alternatives - one is algorithm calculating the degree of similarity between users by utilizing in-degree and out-degree centrality, which are the indices representing the central location in the social network. We named these approaches as 'Trust CF - All' and 'Trust CF - Conditional'. The other alternative is the algorithm reflecting a neighbor's score higher when a target user trusts the neighbor directly or indirectly. The direct or indirect trust relationship can be identified by searching trust network of users. In this study, we call this approach 'Trust CF - Search'. To validate the applicability of the proposed system, we used experimental data provided by LibRec that crawled from the entire FilmTrust website. It consists of ratings of movies and trust relationship network indicating who to trust between users. The experimental system was implemented using Microsoft Visual Basic for Applications (VBA) and UCINET 6. To examine the effectiveness of the proposed system, we compared the performance of our proposed method with one of conventional CF system. The performances of recommender system were evaluated by using average MAE (mean absolute error). The analysis results confirmed that in case of applying without conditions the in-degree centrality index of trusted network of users(i.e. Trust CF - All), the accuracy (MAE = 0.565134) was lower than conventional CF (MAE = 0.564966). And, in case of applying the in-degree centrality index only to the users with the out-degree centrality above a certain threshold value(i.e. Trust CF - Conditional), the proposed system improved the accuracy a little (MAE = 0.564909) compared to traditional CF. However, the algorithm searching based on the trusted network of users (i.e. Trust CF - Search) was found to show the best performance (MAE = 0.564846). And the result from paired samples t-test presented that Trust CF - Search outperformed conventional CF with 10% statistical significance level. Our study sheds a light on the application of user's trust relationship network information for facilitating electronic commerce by recommending proper items to users.

A Study on Research Collaboration Among Asian Countries in Science and Technology (과학기술분야 국제협력 증진을 위한 아시아 국가 간 공동연구 현황 분석)

  • Kim, Won-Jin;Chung, Young-Mee
    • Journal of the Korean Society for information Management
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    • v.27 no.3
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    • pp.103-123
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    • 2010
  • Recently, research community in Korea has shown a rapid growth in collaborating with Asian countries. In this study, we analyzed research collaboration among Asian countries using network analysis of co-authored papers as well as subject categories. The network of co-authored papers among Asian countries over the 5-year period since 2005 revealed that Japan, China, and Korea were positioned at the central part of the network and highly productive in collaborative research. In the analysis of the subject categories of co-authored papers in four different Asian regions with 2009 data, physics and material science were found the most productive subject fields in collaborative research in Northeast Asia. On the other hand, medical science was the most collaborative subject field in the remaining Asian regions.

A Study on Research Trends of Library Science and Information Science Through Analyzing Subject Headings of Doctoral Dissertations Recently Published in the U.S. (학위논문 분석을 통한 미국 도서관학 및 정보과학 최근 연구 동향에 관한 연구)

  • Kim, Hyunjung
    • Journal of the Korean Society for information Management
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    • v.35 no.3
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    • pp.11-39
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    • 2018
  • The study examines the research trends of doctoral dissertations in Library Science and Information Science published in the U.S. for the last 5 years. Data collected from PQDT Global includes 1,016 doctoral dissertations containing "Library Science" or "Information Science" as subject headings, and keywords extracted from those dissertations were used for a network analysis, which helps identifying the intellectual structure of the dissertations. Also, the analysis using 103 subject heading keywords resulted in various centrality measures, including triangle betweenness centrality and nearest neighbor centrality, as well as 26 clusters of associated subject headings. The most frequently studied subjects include computer-related subjects, education-related subjects, and communication-related subjects, and a cluster with information science as the most central subject contains most of the computer-related keywords, while a cluster with library science as the most central subject contains many of the education-related keywords. Other related subjects include various user groups for user studies, and subjects related to information systems such as management, economics, geography, and biomedical engineering.

Topic Analysis of Scholarly Communication Research

  • Ji, Hyun;Cha, Mikyeong
    • Journal of Information Science Theory and Practice
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    • v.9 no.2
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    • pp.47-65
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    • 2021
  • This study aims to identify specific topics, trends, and structural characteristics of scholarly communication research, based on 1,435 articles published from 1970 to 2018 in the Scopus database through Latent Dirichlet Allocation topic modeling, serial analysis, and network analysis. Topic modeling, time series analysis, and network analysis were used to analyze specific topics, trends, and structures, respectively. The results were summarized into three sets as follows. First, the specific topics of scholarly communication research were nineteen in number, including research resource management and research data, and their research proportion is even. Second, as a result of the time series analysis, there are three upward trending topics: Topic 6: Open Access Publishing, Topic 7: Green Open Access, Topic 19: Informal Communication, and two downward trending topics: Topic 11: Researcher Network and Topic 12: Electronic Journal. Third, the network analysis results indicated that high mean profile association topics were related to the institution, and topics with high triangle betweenness centrality, such as Topic 14: Research Resource Management, shared the citation context. Also, through cluster analysis using parallel nearest neighbor clustering, six clusters connected with different concepts were identified.

A study on Typology and Characteristics by Social Activity and Space Usage Condition of the Rural Centers in Population-Decling Era - Focused on Seat of Myeon Office of Rural areas in Jeollanam-do - (인구감소시대 농촌 중심지 내 사회적 활동과 공간의 이용실태에 따른 유형과 특성에 관한 연구 - 전라남도 농촌지역 면소재지를 중심으로 -)

  • Park, Sung-Jin;Yeom, Dae-Bong;Kang, In-Ho
    • Journal of the Korean Institute of Rural Architecture
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    • v.19 no.3
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    • pp.25-32
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    • 2017
  • This study aims to explore the direction of readjustment of rural areas, considering the community. Therefore, it classified the types of 37 Myeon units from 4 communities with higher aging index among rural communities in Jeonnam according to the degree of central functions and population and examined the residents' awareness of community space. As a result, The space used as a social activity for each type is as follows. First of all, the type of decline in the decline of the central index, which reduces transportation conditions, is welfare center (culture center). Second, the central index is high, but there is a low degree of access to the center of the middle and low levels of the population, and the local autonomous center and marketplace, respectively. Third, the low level of access to the central index and the higher center is Nonghyup. Fourth, the decline type with low center index and low accessibility to superior center and low population was the elderly community. Additionally, the types with higher center index on the awareness of community included solidarity and neighbor relationship, and those with lower center index were influenced by cooperation and community awareness for community activation.

Analysis of Reading Domian of Men and Women Elderly Using Book Lending Data (도서 대출데이터를 활용한 남녀 노령자의 독서 주제 분석)

  • Cho, Jane
    • Journal of Korean Library and Information Science Society
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    • v.50 no.1
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    • pp.23-41
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    • 2019
  • This study understand the subject domain of book which has been read by men and woman elderly by analizying the PFNET using library big data and confirm the difference between adult at age 30-40. This study extract co-occurrence matrix of book lending on the popular book list from library big data, for 4 group, men/woman elderly, men/woman adult. With these matrix, this study performs FP network analysis. And Pearson Correlation Analysis based on the Triangle Betweenness Centrality calculated on the loan book was performed to understand the correlation among the 4 clusters which has been created by PNNC algorithm. As a result, reading trend which has been focused on modern korean novel has been revealed in elderly regardless gender, among them, men elderly show extreme tendency concentrated on modern korean long series novel. In the correlation analysis, the male elderly showed a weak negative correlation with the adult male of r = -0.222, and the negative direction of all the other groups showed that the tendency of male elderly's loan book was opposite.