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.
Registration and description of records are important elements of processing which provide with the background information of production of records and business-related information. They also enable to search and use the records. In this paper, I examined the Korean registration and description system defined in the Public Records Management Act which directs the records creating agency to register records in creating offices and directs the "professional archives" to make "basic registrations" and "detailed registrations" of the records. In the analysis and comparison of two different registration and description systems with the known international standards of records and archives management, such as ISO15489 and ISAD(G), I intended to evaluate the Korean records and archives management system and suggested recommendations for the renovation of the Korean recordskeeping system. Despite we have unique office business procedures and the culture of officialdom, and despite we have developed our system based on the established business procedures and office culture, it would be preferable to adopt or follow the international standards and established best practices. After the comparative analysis, I recommended some innovations in the filed of registration and description. For instance, in the basic registration. we would better to install an item of "simple contents summary." We may also need the multiple-level description. The fonds level description and the series level description should be introduced to our archival automated management system. We need to establish a Korean standard of description adopting the rules of the ISAD(G) and ISAAR(CPF). Essential requirements for electronic records management, such as contextual and structural information, should be incorporated in the new standard. Documentation of records disposition also should be reinforced to guarantee the authenticity of records and to ensure control of the records. To implement the recommendations for the standard, we need to amend the Public Records Management Act and its Regulations and Rules. Also it is imperative to redesign the GARS integrated archival automated management system.
It is difficult to evaluate the performance of process innovation of e-procurement which has large scale and complex processes. The existing evaluation methods for measuring the effects of process innovation have been mainly done with statistically quantitative methods by analyzing operational data or with qualitative methods by conducting surveys and interviews. However, these methods have some limitations to evaluate the effects because the performance evaluation of e-procurement process innovation should consider the interactions among participants who are active either directly or indirectly through the processes. This study considers the e-procurement process as a complex system and develops a simulation model based on MAS(Multi-Agent System) to evaluate the effects of e-procurement process innovation. Multi-agent based simulation allows observing interaction patterns of objects in virtual world through relationship among objects and their behavioral mechanism. Agent-based simulation is suitable especially for complex business problems. In this study, we used Netlogo Version 4.1.3 as a MAS simulation tool which was developed in Northwestern University. To do this, we developed a interaction model of agents in MAS environment. We defined process agents and task agents, and assigned their behavioral characteristics. The developed simulation model was applied to G2B system (KONEPS: Korea ON-line E-Procurement System) of Public Procurement Service (PPS) in Korea and used to evaluate the innovation effects of the G2B system. KONEPS is a successfully established e-procurement system started in the year 2002. KONEPS is a representative e-Procurement system which integrates characteristics of e-commerce into government for business procurement activities. KONEPS deserves the international recognition considering the annual transaction volume of 56 billion dollars, daily exchanges of electronic documents, users consisted of 121,000 suppliers and 37,000 public organizations, and the 4.5 billion dollars of cost saving. For the simulation, we analyzed the e-procurement of process of KONEPS into eight sub processes such as 'process 1: search products and acquisition of proposal', 'process 2 : review the methods of contracts and item features', 'process 3 : a notice of bid', 'process 4 : registration and confirmation of qualification', 'process 5 : bidding', 'process 6 : a screening test', 'process 7 : contracts', and 'process 8 : invoice and payment'. For the parameter settings of the agents behavior, we collected some data from the transactional database of PPS and some information by conducting a survey. The used data for the simulation are 'participants (government organizations, local government organizations and public institutions)', 'the number of bidding per year', 'the number of total contracts', 'the number of shopping mall transactions', 'the rate of contracts between bidding and shopping mall', 'the successful bidding ratio', and the estimated time for each process. The comparison was done for the difference of time consumption between 'before the innovation (As-was)' and 'after the innovation (As-is).' The results showed that there were productivity improvements in every eight sub processes. The decrease ratio of 'average number of task processing' was 92.7% and the decrease ratio of 'average time of task processing' was 95.4% in entire processes when we use G2B system comparing to the conventional method. Also, this study found that the process innovation effect will be enhanced if the task process related to the 'contract' can be improved. This study shows the usability and possibility of using MAS in process innovation evaluation and its modeling.
In this study we examined the topic analysis and correlation analysis by text mining to extract meaningful information or rules from the self introduction letter of freshman at Korea National College of Agriculture and Fisheries in 2020. The analysis items are described in items related to 'academic' and 'in-school activities' during high school. In the text mining results, the keywords of 'academic' items were 'study', 'thought', 'effort', 'problem', 'friend', and the key words of 'in-school activities' were 'activity', 'thought', 'friend', 'club', 'school' in order. As a result of the correlation analysis, the key words of 'thinking', 'studying', 'effort', and 'time' played a central role in the 'academic' item. And the key words of 'in-school activities' were 'thought', 'activity', 'school', 'time', and 'friend'. The results of frequency analysis and association analysis were visualized with word cloud and correlation graphs to make it easier to understand all the results. In the next study, TF-IDF(Term Frequency-Inverse Document Frequency) analysis using 'frequency of keywords' and 'reverse of document frequency' will be performed as a method of extracting key words from a large amount of documents.
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