Min-Sung Hong

Min-Sung Hong
Vestlandsforsking | WRNI · Department of Information Technology

Doctor of Engineering

About

47
Publications
6,016
Reads
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289
Citations
Additional affiliations
February 2018 - present
Vestlandsforsking
Position
  • Researcher
Description
  • Working at the field of Big Data for transport sector and emergency management
March 2015 - February 2018
Chung-Ang University
Position
  • PhD Student
March 2012 - January 2014
Dankook University
Position
  • assistant instructor of department

Publications

Publications (47)
Article
Full-text available
With the advance of sentiment analysis techniques, several studies have been on Multi-Criteria Recommender Systems (MCRS) leveraging sentiment information. However, partial preferences quite and naturally happen in MCRS and negatively affect the predictive performances of sentiment analysis and multi-criteria recommendation. In this paper, we propo...
Article
In the recommender system field, diversity as the measure of recommendation quality has gained much attention recently. However, many pieces of research have shown that it has a trade-off relation with predictive performance. To improve recommendation diversity and predictive performance in multi-criteria recommender systems, we propose a clusterin...
Article
Although spatial and temporal information has often been considered to improve recommendation performances, existing multi-criteria recommender systems often neglect to leverage spatial and temporal information. Also, it is a non-trivial task to simultaneously apply such information to recommendation services since the factors have interrelations t...
Article
Many tourism recommender systems have been studied to offer users the items meeting their interests. However, it is a non-trivial task to reflect the multi-criteria ratings and the cultural differences, which significantly influence users’ reviews of tourism facilities, into recommendation services. This paper proposes two “single tensor” models, c...
Conference Paper
Full-text available
Artificial intelligence (AI) represents huge opportunities for us as individuals and for society at large. Recently global momentum around AI for social good is growing. AI opens a new perspective to maintain public security and safety by providing investigative assistance with a human-grade precision. Quantitative methods might always not be a cor...
Chapter
The term emergency management is used in this book to encompass all of the activities carried out by the federal state and local agencies that are referred to as emergency services. These activities have the primary goal of managing hazards, risks, and emergencies of all types. The advances in information and communication technology have a profoun...
Chapter
Machine learning techniques can help authorities and decision makers more accurately answer urgent questions. Machine learning can be used to refine strategies over time, getting smarter about planning and response. This chapter discusses the application of fundamental learning techniques to support the decision making processes for emergency manag...
Article
Full-text available
This paper provides a new approach that improves collaborative filtering results in recommendation systems. In particular, we aim to ensure the reliability of the data set collected which is to collect the cognition about the item similarity from the users. Hence, in this work, we collect the cognitive similarity of the user about similar movies. B...
Article
Full-text available
The previous recommendation system applied the matrix factorization collaborative filtering (MFCF) technique to only single domains. Due to data sparsity, this approach has a limitation in overcoming the cold-start problem. Thus, in this study, we focus on discovering latent features from domains to understand the relationships between domains (cal...
Article
Full-text available
Multi-Criteria Recommender Systems (MCRSs) have been developed to improve the accuracy of single-criterion rating-based recommender systems that could not express and reflect users? fine-grained rating behaviors. In most MCRSs, new users are asked to express their preferences on multi-criteria of items, to ad15 dress the cold-start problem. However...
Article
Full-text available
Cultural Heritage (CH) domain is rapidly moving from traditional heritage sites into smart cultural heritage environment through various technologies. As one of the important technologies in the smart space, Recommender Systems (RSs) have been widely utilised to personalised services and matching visitors’ goals and behaviours. Whereas, cultural di...
Article
Disasters pose a serious threat to people’ lives and urban environment, affecting the sustainable development of society. Then it's crucial to quickly develop an efficient rescue plan for the disaster area. However, disaster rescue is rather difficult due to the requirement to develop the optimal rescue plan as quickly as possible according to the...
Chapter
The advances in information technology have had a profound impact on emergency management by making unprecedented volumes of data available to the decision makers. This has resulted in new challenges related to the effective management of large volumes of data. In this regard, the role of machine learning in mass emergency and humanitarian crises i...
Technical Report
Full-text available
The deliverable presents seven reports of the case studies conducted in Work Package 3 during Task 3.2. The case studies conducted are the following: • Case study 1 “Railway transport” • Case study 2 “Open data and the transport sector” • Case study 3 “Real-time traffic management” • Case study 4 “Logistics and consumer preferences” • Case study 5...
Article
In this paper, we propose an interdisciplinary approach to (natural) disaster relief management. Our framework combines dynamic and static databases, which consist of social media and authoritative data of an afflicted region, respectively, to model rescue demand during a disaster situation. Using Global Particle Swarm Optimization and Mixed-Intege...
Article
The advent of Internet of Things (IoT) paradigm is having a great ripple effect in the field of disaster management as well as many other areas. Victim detection has been studied as rescuer‐ or victim‐oriented approach, which utilises the IoT advanced technologies, but they have weaknesses according to disaster types. In this paper, we propose a Vi...
Article
Recently, explainable recommender systems to improve their persuasiveness have attracted attentions. In this regard, some approaches extract information from posts or comments on items and apply them to simple and effective template. These information (e.g., topics and interests), however, are indirectly reflected to the existing recommendation alg...
Chapter
Data is growing at an alarming rate. This growth is spurred by varied array of sources, such as embedded sensors, social media sites, video cameras, the quantified-self and the internet-of-things. This is changing our reliance on data for making decisions, or data analytics, from being mostly carried out by an individual and in limited settings to...
Article
Full-text available
Social big data is currently an emergent issue, especially for recommender systems. In particular, with respect to social big data, various data mining techniques have been applied in group recommender systems. However, three social phenomena (i.e., social influence, emotional contagion, and conformity) have not been applied enough in existing stud...
Article
Tensor factorization has been applied in recommender systems to discover latent factors between multidimensional data such as time, place, and social context. However, tensor-based recommender systems still encounter with several problems such as sparsity, cold-start, and so on. In this paper, we introduce the new model social tensor to propose a t...
Article
A large amount of time-series data has been frequently used to extract the useful patterns and trends and to visualize them for better understanding. This work is focusing on visualizing personal lifelogging data for tracking back to personal histories. Thereby, we present several similarity measures between multidimensional data at two different t...
Conference Paper
Recently, most of context-aware services are trying to exploit the emotional contexts of the target users. The aim of this conceptual paper is to discuss affective lifelogging framework which can recognize the emotions by integrating multimodal information from multiple sources. Moreover, we will mention the open problems on affective lifelogging.
Conference Paper
It is important to understand customers (e.g., preferences) for box office prediction. Recently, many studies have focused on analyzing SNS data to predict box office. However, the studies have problems as follows: 1) decreasing the performance of prediction by the characteristics of SNS, and 2) increasing computational cost of prediction which is...
Conference Paper
It is important to scout a foreign player for the Korea Baseball Organization (KBO) league. However, a scouting the foreign player, based on the traditional methods, physically causes the high cost. To alleviate this problem, in this paper, we propose a foreign player recommendation (FPR) method based on Ant Colony Optimization (ACO) to efficiently...
Article
Existing group recommender systems generate a consensus function to aggregate individual preference into group preference. However, the systems encounter difficulty in gathering rating-scores and validating their reliability, since the aggregation strategy requires user rating-scores. To solve these problems, we propose Group Recommendation based o...
Article
Full-text available
Storification is a theoretical technique which aims to construct the underlying relationships from discrete information for packaging them into a logical structure. In this paper, we focus on proposing the definition of serendipity-based storification in the personal history which is the combination of two-step processes: i) discovering hidden stor...
Article
Full-text available
Cultural Heritage is a domain in which new technologies and services have a special impact on people approach to its spaces. Technologies are changing the role of such spaces, allowing a more in-depth knowledge diffusion and social interactions. Static places become dynamic cultural environments in which people can discover and share new knowledge....
Chapter
Full-text available
In this paper, we propose a new traffic system recommendation based on support real-time flows in highly unpredictable sensor network environments. The approach system is real-time recommendation system which meet various demands of users. The proposed algorithm include two phases. First phase is proposed to deal with the real-time problem. By this...
Article
Full-text available
The large amount of information that is currently being collected (the so-called “big data”), have resulted in model-based Collaborative Filtering (CF) methods to encountering limitations, e.g., the sparsity problem and the scalability problem. It is difficult for model-based CF methods to address the scalability-performance tradeoff. Therefore, we...
Conference Paper
Full-text available
데이터 마이닝와 시각화 분야에서 대량의 시간적인 데이터에 대한 유의미한 정보 추출과 시각화는 많이 연구되고 있다. 하지만 개인의 히스토리에 대한 연구는 미비하다. 따라서 본 연구에서는 사용자의 히스토리의 영화 간 유사도를 바탕으로 시간에 따른 사용자의 영화 취향의 변화를 추출하고 시각화하는 방법을 제안한다.
Article
Social network information has recently been used for the improvement of the performances of recommender systems with regard to both individual users and groups. During the selection of the items for a group, the role of the corresponding relationships (e.g., position, dependency, and the strength of the social ties) is often more important than th...
Conference Paper
Full-text available
Information collected from the social network is recently used to improve a performance of recommender systems to an individual user or a group. During selecting the items among the group members, the relationships (e.g., position , dependency, and the strength of the social ties) often has an important role than the individual preference in the gr...
Chapter
Full-text available
Complex Event Processing (CEP) detects complex events or patterns of event sequences based on a set of rules defined by a domain expert. However, it lowers the reliability of a system as the set of rules defined by an expert changes along with dynamic changes in the domain environment. A human error made by an expert is another factor that may unde...
Conference Paper
Full-text available
Collaborative Filtering(CF) technique based on user is the one of the method widely used by recommender systems but they have some problems. One of these mainly problems is data sparsity. The other one is scalability. In this paper, we propose an Adaptive Clustering for Scalable Collaborative Filtering Technique. We show that our approach outperfor...
Article
Full-text available
The method for personalized wellness-content recommendation has actively studied in the field of information technology convergence. But a problem with low reliability of recommendations has emerged. Because existing studies deal with only one or two areas of wellness, to solve the reliability problems, a study is needed into an integration techniq...
Article
Full-text available
Research into recommendation systems for wellness content has focused on representative research on the convergence of wellness and information technology, as interest in wellness has recently increased. But existing research is not suitable because it uses only one or two of the five wellness areas: physical, emotional, social, intellectual, and s...
Article
Full-text available
An Adaptive Clustering-based Collaborative Filtering Technique was proposed to solve the fundamental problems of collaborative filtering, such as cold-start problems, scalability problems and data sparsity problems. Previous collaborative filtering techniques were carried out according to the recommendations based on the predicted preference of the...
Conference Paper
Full-text available
Research into recommendation systems for wellness content has focused on representative research on the convergence of wellness and information technology, as interest in wellness has recently increased. But existing research is not suitable because it uses only one or two of the five wellness areas: physical, emotional, social, intellectual, and s...

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