• Asia pacific journal of information systems
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• v.14 no.4
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• pp.87-97
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• 2004

One of the most fundamental problems in business is ranking items with respect to profit based on historical transactions. The difficulty is that the profit of one item comes from its influence on the sales of other items as well as its own sales, and that there is no well-developed algorithm for estimating overall profit of selected items. In this paper, we developed a product network based on association rule and an algorithm for profit estimation and item selection using the estimated profit ranking(EPR). As a result of computer simulation, the suggested algorithm outperforms the individual approach and the hub-authority profit ranking algorithm.

• International Journal of Advanced Culture Technology
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• v.11 no.2
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• pp.358-365
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• 2023

As the Internet is used as a major channel for marketing and sales, the top ranking of search engine results is becoming a key competitor among websites. Various methods exist to maintain the top ranking of websites in search engines, typically investing heavily in organic coding or search engine optimization. The purpose of this paper, we present the ranking by recognizing factors that should be removed as negative factors when designing a web page in consideration of website visibility (SEO) because if website visibility is not met, the ranking may fall behind or be completely removed from the search engine index. The experiments that recognized and ranked the negative factors of website visibility proposed in this paper were provided through theory and experiments based on the existing website visibility analysis model. The models analyzed in this paper, we expressed or quantified as scores based on the methodology of each model, and 10 items were selected as negative factors through experiments and ranked as high scores. Therefore, when designing a website, it should be considered that the website is not removed from the search engine index as it is designed by excluding high-ranking items, which are negative factors.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.12
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• pp.5826-5841
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• 2019

In our previous research, we proposed a tag emotion-based item recommendation scheme. The ternary associations among users, items, and tags are described as a three-order tensor in order to capture the emotions in tags. The candidates for recommendation are created based on the latent semantics derived by a high-order singular value decomposition technique (HOSVD). However, the tensor is very sparse because the number of tagged items is smaller than the amount of all items. The previous research do not consider the previous behaviors of users and items. To mitigate the problems, in this paper, the item-based collaborative filtering scheme is used to build an extended data. We also apply the probabilistic ranking algorithm considering the user and item profiles to improve the recommendation performance. The proposed method is evaluated based on Movielens dataset, and the results show that our approach improves the performance compared to other methods.

• Journal of Food Hygiene and Safety
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• v.24 no.3
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• pp.200-203
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• 2009

The risk ranking of food groups included the Korea Food Code is a potentially powerful means to prioritize food safety management strategies. Although the interest in risk ranking of food groups has been increasing worldwide, there was, until recently, no standard system in Korea for the risk ranking of food groups. This study was conducted to rank food groups using theoretically estimated comparative risk scores of 101 food groups included the Korea Food Code. These scores were estimated using the risk evaluation model, which focuses on 3 aspects, namely, exposure assessment, severity assessment, and consumption part. The results of this study revealed that the risk was the highest in the case of ready-to-eat (RTE) food items, followed by fish products and breads. Using this ranking system, we can identify the food with high risk scores and design risk management strategies targeted specifically at these items.

• Survey Research
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• v.9 no.2
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• pp.29-49
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• 2008

Many survey data are collected today to measure personal values and to order them according to their importance. There are two popular procedures to achieve the goal: ranking procedures and rating procedures. The ranking procedures can be divided into two categories; full ranking procedures and reduced ranking procedures. The reduced ranking procedure is more often used because of its easiness to respondents. However, the ordered responses are not generally incorporated into ordering their values. This research has studied ways to incorporate the ordered responses into ordering the values. We have considered the ranking scales as the conditional rating scales. Our findings are that the ordering values based on the weighted proportions is better than one based on the unweighted proportions.

• Convergence Security Journal
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• v.18 no.4
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• pp.19-25
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• 2018

In this paper, we propose an user-centered integrated lost-goods management system through a ranking system based on weight and a hierarchical image classification system based on Deep Learning. The proposed system consists of a hierarchical image classification system that automatically classifies images through deep learning, and a ranking system modules that listing the registered lost property information on the system in order of weight for the convenience of the query process.In the process of registration, various information such as category classification, brand, and related tags are automatically recognized by only one photograph, thereby minimizing the hassle of users in the registration process. And through the ranking systems, it has increased the efficiency of searching for lost items by exposing users frequently visited lost items on top. As a result of the experiment, the proposed system allows users to use the system easily and conveniently.

• The Mathematical Education
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• v.38 no.2
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• pp.129-144
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• 1999

The purpose of our work is to developing the program of math. camp to improve underachiever's mathematical disposition. To do this, the following research were taken; (1)Analysis of current status of programs for underachievers (2)Analysis of inclination to mathematics(We collected the data from 2 classes of middle schools) (3)Prepare and apply the program of math. camp for the students including underachievers, and then analysis the effect of the math. camp The results of this study is as follows; (1)Only 40% of investigated schools have their own programs for underachievers. But almost all general high schools do not have such programs because students do not want. More than half of the investigated teachers suggested that the most important thing for underachievers is the induction of motivation for mathematics. (2)Many students dislike mathematics from 5∼6 grade of elementary school, and more than 50% of students think that 'measure' and 'equations' items are difficult. (3) After attending the math. camp based on the games and activities in small groups, the students in the middle-ranking group showed more positive reactions against the items of mathematical disposition and attitude tests. The students in the row-ranking group were improved in the 'self-confidence' and 'will' items of mathematical disposition test and in the 'superiority' and 'interest' items of mathematical attitude test.

• Asia pacific journal of information systems
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• v.25 no.4
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• pp.686-711
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• 2015

The performance of a novel medical question-answering engine called CliniCluster and existing search engines, such as CQA-1.0, Google, and Google Scholar, was evaluated using known-item searching. Known-item searching is a document that has been critically appraised to be highly relevant to a therapy question. Results show that, using CliniCluster, known-items were retrieved on average at rank 2 ($MRR@10{\approx}0.50$), and most of the known-items could be identified from the top-10 document lists. In response to ill-defined questions, the known-items were ranked lower by CliniCluster and CQA-1.0, whereas for Google and Google Scholar, significant difference in ranking was not found between well- and ill-defined questions. Less than 40% of the known-items could be identified from the top-10 documents retrieved by CQA-1.0, Google, and Google Scholar. An analysis of the top-ranked documents by strength of evidence revealed that CliniCluster outperformed other search engines by providing a higher number of recent publications with the highest study design. In conclusion, the overall results support the use of CliniCluster in answering therapy questions by ranking highly relevant documents in the top positions of the search results.

• Proceedings of the KSR Conference
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• 1998.05a
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• pp.184-193
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• 1998

The existence of railroad line gives many influences in various kinds of aspects. To minimize negative influences is necessary to line's improvement, but appropriate methodology doesn't show until now. In this study, Fuzzy Integration Method(FIM) are employed in an effort to give ranking for railroad line's improvement in Seoul. The FIM is designed to generalize a various influences, appeared on account of existence of railroad line. Empirical analysis is performed for railroad line in distance of 83.5㎞ in Seoul. The total lines are divided in 51 sections, and there are selected a 11 evaluation index to reflect influences. Through a questionnaire survey about residents, operator and administrator, important degree of evaluation items are decided, reveal ins the interests of related groups. Then, evaluation values are calculated wi th practical survey results about each sections. The results of evaluation reveal that the higher ranking of improvement from FIM concentrates the Kyeng-Ul Line and Kyeng-Won line because these lines appear a many public discontents and negative influences such as noise and demolition of living environment.

• Journal of KIISE:Software and Applications
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• v.37 no.9
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• pp.659-668
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• 2010

Collaborative filtering systems grasp behaviors for a new user and need new information for the user in order to recommend interesting items to the user. For the purpose of acquiring the information the collaborative filtering systems learn behaviors for users based on the previous data and can obtain new information from the results. In this paper, we propose an inductive inference method to obtain new information for users and rank items by using the new information in the proposed method. The proposed method clusters users into groups by learning users through NMF among inductive machine learning methods and selects the group features from the groups by using chi-square. Then, the method classifies a new user into a group by using the bayesian probability model as one of inductive inference methods based on the rating values for the new user and the features of groups. Finally, the method decides the ranks of items by applying the Rocchio algorithm to items with the missing values.