• Journal of Information Processing Systems
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• v.13 no.4
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• pp.778-804
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• 2017

Software today has become an inseparable part of our life. In order to achieve the ever demanding needs of customers, it has to rapidly evolve and include a number of changes. In this paper, our aim is to study the relationship of object oriented metrics with change proneness attribute of a class. Prediction models based on this study can help us in identifying change prone classes of a software. We can then focus our efforts on these change prone classes during testing to yield a better quality software. Previously, researchers have used statistical methods for predicting change prone classes. But machine learning methods are rarely used for identification of change prone classes. In our study, we evaluate and compare the performances of ten machine learning methods with the statistical method. This evaluation is based on two open source software systems developed in Java language. We also validated the developed prediction models using other software data set in the same domain (3D modelling). The performance of the predicted models was evaluated using receiver operating characteristic analysis. The results indicate that the machine learning methods are at par with the statistical method for prediction of change prone classes. Another analysis showed that the models constructed for a software can also be used to predict change prone nature of classes of another software in the same domain. This study would help developers in performing effective regression testing at low cost and effort. It will also help the developers to design an effective model that results in less change prone classes, hence better maintenance.

• Journal of Broadcast Engineering
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• v.14 no.5
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• pp.573-588
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• 2009

In this paper, we propose a novel video quality degradation monitoring scheme titled VR-VQMS(Visual Rhythm based Video Quality Monitoring Scheme) over an IPTV service prone to packet losses during network transmission. Proposed scheme quantifies the amount of quality degradation due to packet losses, and can be classified into a RR(reduced-reference) based quality measurement scheme exploiting visual rhythm data of H.264-encoded video frames at a media server and reconstructed ones at an Set-top Box as feature information. Two scenarios, On-line and Off-line VR-VQMS, are proposed as the practical solutions. We define the NPSNR(Networked Peak-to-peak Signal-to-Noise Ratio) modified by the well-known PSNR as a new objective quality metric, and several additional objective and subjective metrics based on it to obtain the statistics on timing, duration, occurrence, and amount of quality degradation. Simulation results show that the proposed method closely approximates the results from 2D video frames and gives good estimation of subjective quality(i.e.,MOS(mean opinion score)) performed by 10 test observers. We expect that the proposed scheme can play a role as a practical solution to monitor the video quality experienced by individual customers in a commercial IPTV service, and be implemented as a small and light agent program running on a resource-limited set-top box.

• Journal of Intelligence and Information Systems
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• v.27 no.3
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• pp.95-112
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• 2021

In university education, the choice of major class plays an important role in students' careers. However, in line with the changes in the industry, the fields of major subjects by department are diversifying and increasing in number in university education. As a result, students have difficulty to choose and take classes according to their career paths. In general, students choose classes based on experiences such as choices of peers or advice from seniors. This has the advantage of being able to take into account the general situation, but it does not reflect individual tendencies and considerations of existing courses, and has a problem that leads to information inequality that is shared only among specific students. In addition, as non-face-to-face classes have recently been conducted and exchanges between students have decreased, even experience-based decisions have not been made as well. Therefore, this study proposes a recommendation system model that can recommend college major classes suitable for individual characteristics based on data rather than experience. The recommendation system recommends information and content (music, movies, books, images, etc.) that a specific user may be interested in. It is already widely used in services where it is important to consider individual tendencies such as YouTube and Facebook, and you can experience it familiarly in providing personalized services in content services such as over-the-top media services (OTT). Classes are also a kind of content consumption in terms of selecting classes suitable for individuals from a set content list. However, unlike other content consumption, it is characterized by a large influence of selection results. For example, in the case of music and movies, it is usually consumed once and the time required to consume content is short. Therefore, the importance of each item is relatively low, and there is no deep concern in selecting. Major classes usually have a long consumption time because they have to be taken for one semester, and each item has a high importance and requires greater caution in choice because it affects many things such as career and graduation requirements depending on the composition of the selected classes. Depending on the unique characteristics of these major classes, the recommendation system in the education field supports decision-making that reflects individual characteristics that are meaningful and cannot be reflected in experience-based decision-making, even though it has a relatively small number of item ranges. This study aims to realize personalized education and enhance students' educational satisfaction by presenting a recommendation model for university major class. In the model study, class history data of undergraduate students at University from 2015 to 2017 were used, and students and their major names were used as metadata. The class history data is implicit feedback data that only indicates whether content is consumed, not reflecting preferences for classes. Therefore, when we derive embedding vectors that characterize students and classes, their expressive power is low. With these issues in mind, this study proposes a Net-NeuMF model that generates vectors of students, classes through network analysis and utilizes them as input values of the model. The model was based on the structure of NeuMF using one-hot vectors, a representative model using data with implicit feedback. The input vectors of the model are generated to represent the characteristic of students and classes through network analysis. To generate a vector representing a student, each student is set to a node and the edge is designed to connect with a weight if the two students take the same class. Similarly, to generate a vector representing the class, each class was set as a node, and the edge connected if any students had taken the classes in common. Thus, we utilize Node2Vec, a representation learning methodology that quantifies the characteristics of each node. For the evaluation of the model, we used four indicators that are mainly utilized by recommendation systems, and experiments were conducted on three different dimensions to analyze the impact of embedding dimensions on the model. The results show better performance on evaluation metrics regardless of dimension than when using one-hot vectors in existing NeuMF structures. Thus, this work contributes to a network of students (users) and classes (items) to increase expressiveness over existing one-hot embeddings, to match the characteristics of each structure that constitutes the model, and to show better performance on various kinds of evaluation metrics compared to existing methodologies.

• Asia pacific journal of information systems
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• v.24 no.4
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• pp.493-513
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• 2014

With the development of related technologies, Location-Based Services (LBS) are growing fast and being used in many ways. Past LBS studies have focused on adoption of LBS because of the fact that LBS users have privacy concerns regarding revealing their location information. Meanwhile, the number of LBS users and revenues from LBS are growing rapidly because users can get some benefits by revealing their location information. Little research has been done on how LBS affects consumers' information search behavior in product purchase. The purpose of this paper is examining the effect of LBS information filtering on buyers' uncertainty and their information search behavior. When consumers purchase a product, they try to reduce uncertainty by searching information. Generally, there are two types of uncertainties - knowledge uncertainty and choice uncertainty. Knowledge uncertainty refers to the lack of information on what kinds of alternatives are available in the market and/or their important attributes. Therefore, consumers having knowledge uncertainty will have difficulties in identifying what alternatives exist in the market to fulfil their needs. Choice uncertainty refers to the lack of information about consumers' own preferences and which alternative will fit in their needs. Therefore, consumers with choice uncertainty have difficulties selecting best product among available alternatives.. According to economics of information theory, consumers narrow the scope of information search when knowledge uncertainty is high. It is because consumers' information search cost is high when their knowledge uncertainty is high. If people do not know available alternatives and their attributes, it takes time and cognitive efforts for them to acquire information about available alternatives. Therefore, they will reduce search breadth. For people with high knowledge uncertainty, the information about products and their attributes is new and of high value for them. Therefore, they will conduct searches more in-depth because they have incentive to acquire more information. When people have high choice uncertainty, people tend to search information about more alternatives. It is because increased search breadth will improve their chances to find better alternative for them. On the other hand, since human's cognitive capacity is limited, the increased search breadth (more alternatives) will reduce the depth of information search for each alternative. Consumers with high choice uncertainty will spend less time and effort for each alternative because considering more alternatives will increase their utility. LBS provides users with the capability to screen alternatives based on the distance from them, which reduces information search costs. Therefore, it is expected that LBS will help users consider more alternatives even when they have high knowledge uncertainty. LBS provides distance information, which helps users choose alternatives appropriate for them. Therefore, users will perceive lower choice uncertainty when they use LBS. In order to test the hypotheses, we selected 80 students and assigned them to one of the two experiment groups. One group was asked to use LBS to search surrounding restaurants and the other group was asked to not use LBS to search nearby restaurants. The experimental tasks and measures items were validated in a pilot experiment. The final measurement items are shown in Appendix A. Each subject was asked to read one of the two scenarios - with or without LBS - and use a smartphone application to pick a restaurant. All behaviors on smartphone were recorded using a recording application. Search breadth was measured by the number of restaurants clicked by each subject. Search depths was measured by two metrics - the average number of sub-level pages each subject visited and the average time spent on each restaurant. The hypotheses were tested using SPSS and PLS. The results show that knowledge uncertainty reduces search breadth (H1a). However, there was no significant correlation between knowledge uncertainty and search depth (H1b). Choice uncertainty significantly reduces search depth (H2b), but no significant relationship was found between choice uncertainty and search breadth (H2a). LBS information filtering significantly reduces the buyers' choice uncertainty (H4) and reduces the negative relationship between knowledge uncertainty and search breadth (H3). This research provides some important implications for service providers. Service providers should use different strategies based on their service properties. For those service providers who are not well-known to consumers (high knowledge uncertainty) should encourage their customers to use LBS. This is because LBS would increase buyers' consideration sets when the knowledge uncertainty is high. Therefore, less known services have chances to be included in consumers' consideration sets with LBS. On the other hand, LBS information filtering decrease choice uncertainty and the near service providers are more likely to be selected than without LBS. Hence, service providers should analyze geographically approximate competitors' strength and try to reduce the gap so that they can have chances to be included in the consideration set.

• Journal of Intelligence and Information Systems
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• v.24 no.2
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• pp.85-109
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• 2018

Recommender system recommends the items expected to be purchased by a customer in the future according to his or her previous purchase behaviors. It has been served as a tool for realizing one-to-one personalization for an e-commerce service company. Traditional recommender systems, especially the recommender systems based on collaborative filtering (CF), which is the most popular recommendation algorithm in both academy and industry, are designed to generate the items list for recommendation by using 'overall rating' - a single criterion. However, it has critical limitations in understanding the customers' preferences in detail. Recently, to mitigate these limitations, some leading e-commerce companies have begun to get feedback from their customers in a form of 'multicritera ratings'. Multicriteria ratings enable the companies to understand their customers' preferences from the multidimensional viewpoints. Moreover, it is easy to handle and analyze the multidimensional ratings because they are quantitative. But, the recommendation using multicritera ratings also has limitation that it may omit detail information on a user's preference because it only considers three-to-five predetermined criteria in most cases. Under this background, this study proposes a novel hybrid recommendation system, which selectively uses the results from 'traditional CF' and 'CF using multicriteria ratings'. Our proposed system is based on the premise that some people have holistic preference scheme, whereas others have composite preference scheme. Thus, our system is designed to use traditional CF using overall rating for the users with holistic preference, and to use CF using multicriteria ratings for the users with composite preference. To validate the usefulness of the proposed system, we applied it to a real-world dataset regarding the recommendation for POI (point-of-interests). Providing personalized POI recommendation is getting more attentions as the popularity of the location-based services such as Yelp and Foursquare increases. The dataset was collected from university students via a Web-based online survey system. Using the survey system, we collected the overall ratings as well as the ratings for each criterion for 48 POIs that are located near K university in Seoul, South Korea. The criteria include 'food or taste', 'price' and 'service or mood'. As a result, we obtain 2,878 valid ratings from 112 users. Among 48 items, 38 items (80%) are used as training dataset, and the remaining 10 items (20%) are used as validation dataset. To examine the effectiveness of the proposed system (i.e. hybrid selective model), we compared its performance to the performances of two comparison models - the traditional CF and the CF with multicriteria ratings. The performances of recommender systems were evaluated by using two metrics - average MAE(mean absolute error) and precision-in-top-N. Precision-in-top-N represents the percentage of truly high overall ratings among those that the model predicted would be the N most relevant items for each user. The experimental system was developed using Microsoft Visual Basic for Applications (VBA). The experimental results showed that our proposed system (avg. MAE = 0.584) outperformed traditional CF (avg. MAE = 0.591) as well as multicriteria CF (avg. AVE = 0.608). We also found that multicriteria CF showed worse performance compared to traditional CF in our data set, which is contradictory to the results in the most previous studies. This result supports the premise of our study that people have two different types of preference schemes - holistic and composite. Besides MAE, the proposed system outperformed all the comparison models in precision-in-top-3, precision-in-top-5, and precision-in-top-7. The results from the paired samples t-test presented that our proposed system outperformed traditional CF with 10% statistical significance level, and multicriteria CF with 1% statistical significance level from the perspective of average MAE. The proposed system sheds light on how to understand and utilize user's preference schemes in recommender systems domain.