• Journal of Information Technology Services
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• v.21 no.2
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• pp.109-126
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• 2022

The number and variety of products and services offered by companies have increased dramatically, providing customers with more choices to meet their needs. As a solution to this information overload problem, the provision of tailored services to individuals has become increasingly important, and the personalized recommender systems have been widely studied and used in both academia and industry. Existing recommender systems face important problems in practical applications. The most important problem is that it cannot clearly explain why it recommends these products. In recent years, some researchers have found that the explanation of recommender systems may be very useful. As a result, users are generally increasing conversion rates, satisfaction, and trust in the recommender system if it is explained why those particular items are recommended. Therefore, this study presents a methodology of providing an explanatory function of a recommender system using a review text left by a user. The basic idea is not to use all of the user's reviews, but to provide them in a summarized form using only reviews left by similar users or neighbors involved in recommending the item as an explanation when providing the recommended item to the user. To achieve this research goal, this study aims to provide a product recommendation list using user-based collaborative filtering techniques, combine reviews left by neighboring users with each product to build a model that combines text summary methods among deep learning-based natural language processing methods. Using the IMDb movie database, text reviews of all target user neighbors' movies are collected and summarized to present descriptions of recommended movies. There are several text summary methods, but this study aims to evaluate whether the review summary is well performed by training the Sequence-to-sequence+attention model, which is a representative generation summary method, and the BertSum model, which is an extraction summary model.

• Asia pacific journal of information systems
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• v.15 no.3
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• pp.223-241
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• 2005

In spite of the rapid growth of mobile multimedia contents market, most of the customers experience inconvenience, lengthy search processes and frustration in searching for the specific multimedia contents they want. These difficulties are attributable to the current mobile Internet service method based on inefficient sequential search. To overcome these difficulties, this paper proposes a MOBIIe COntents Recommender System for Movie(MOBICORS-Movie), which is designed to reduce customers' search efforts in finding desired movies on the mobile Internet. MOBICORS-Movie consists of three agents: CF(Collaborative Filtering), CBIR(Content-Based Information Retrieval) and RF(Relevance Feedback). These agents collaborate each other to support a customer in finding a desired movie by generating personalized recommendations of movies. To verify the performance of MOBICORS-Movie, the simulation-based experiments were conducted. The results from this experiments show that MOBICORS-Movie significantly reduces the customer's search effort and can be a realistic solution for movie recommendation in the mobile Internet environment.

• Journal of the Korea Society of Computer and Information
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• v.11 no.1 s.39
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• pp.293-300
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• 2006

The Recommender systems help users to find and evaluate items of interest. Such systems have become powerful tools in the domains from electronic commerce to digital libraries and knowledge management. Sellers can recommend products to customers with the prediction of future buying behavior on the basis of the consumer's population statistics and past selling behavior. In this paper, we are describing the design and the development of personalization recommender system which increases satisfaction level of customers by searching products to reflect the pattern and propensity of customers properly. The suggested system supplies the real-time analysis service to predict the customers purchase situation by applying the association rule of the data mining.

• Journal of the Korean Society for information Management
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• v.39 no.2
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• pp.299-324
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• 2022

In this study, in order to explore the contextual elements of book use that were overlooked in the existing book recommender system research, for 15 avid readers with various book search backgrounds, the contents generated in 6 book search situations were collected through the think-aloud protocol. By using content analysis from the collected book use contents, not only the internal and external appeal factors affecting book use, based on the 'appeal factor', the theoretical concept of the readers' advisory service, but also information sources and search methods regarding book use were identified and categorized. The results of this study can be used to extract and reflect meaningful attribute data in the future book recommender system design process.

• Information Systems Review
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• v.20 no.3
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• pp.73-92
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• 2018

Many users are uploading their daily life activities on SNS and use hashtags to describe their postings. Hashtag has the advantage of letting users specify categories for their postings, however until now, the users has had to manually input the hashtags which has been very inconvenient for them. Therefore, in order to address this issue, this paper proposes a hashtag recommender system which recommends proper hashtags to users based on their uploaded images on SNS. The proposed system is designed using four analytic structures, which is composed of a camera information-based analysis, an address-based analysis, a location based CF analysis, and an image-based analysis. In order to check whether the proposed system is improved compared to the existing systems in terms of the hashtag recommendation function, we conducted an evaluation with 212 SNS users from fifteen countries. As a result of the evaluation process, the proposed system shows very high accuracy recommendation results compared to the existing hashtag recommender systems.

• Journal of Internet Computing and Services
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• v.11 no.3
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• pp.121-129
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• 2010

In recent years, Cloud computing is gaining much popularity as it can efficiently utilize the computing resources and hence can contribute to the issue of green IT. So to make the Cloud services commercialized, Cloud markets are necessary and are being developed. As the increasing numbers of various Cloud services are rapidly evolving in the Cloud market, how to select the best and optimal services will be a great challenge. In this paper we present a Cloud service selection framework in the Cloud market that uses a recommender system (RS) which helps a user to select the best services from different Cloud providers (CP) that matches his/her requirements. The RS recommends a service based on the QoS and Virtual Machine (VM) factors of difference CPs. The experimental results show that our Cloud service recommender system (CSRS) can effectively recommend a good combination of Cloud services to consumers.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.46 no.3
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• pp.15-23
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• 2009

In ubiquitous environments, invisible devices and software are connected to one another to provide convenient services to users. In this environments, users want to get a variety of customized services by using only an individual mobile device which has limitations such as tiny display screens, limited input, and less powerful processors. Therefore, The device sharing for solving these limitation problems and its efficient processing is one of the new research topics. This paper proposes a device recommender system which searches and recommends devices for composing user requested services. The device recommender system infers devices based on environmental context of a user. However, customized devices for each user are different because of a variety of user preference even if users want to get the same service in the same space, Therefore the paper considers the user preference for device recommendation. Our device recommender system is implemented and tested on the real mobile object developed for device sharing in ubiquitous environments. Therefore we can expect that the system will be adaptable in real device sharing environments.

• Journal of Intelligence and Information Systems
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• v.13 no.4
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• pp.45-63
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• 2007

Recently as ubiquitous environment comes to the fore, information density is raised and enterprise is being able to capture and utilize customer-related information at the same time when the customer purchases a product. In this environment, a need for the recommender systems which can deliver proper information to the customer at the right time and right situation is highly increased. Therefore, the research on recommender systems continued actively in a variety of fields. Until now, most of recommender systems deal with item recommendation. However, in the market in ubiquitous environment where the same item can be purchased at several stores, it is highly desirable to recommend store to the customer based on his/her contextual situation and preference such as store location, store atmosphere, product quality and price, etc. In this line of research, we proposed the store recommender system using customer's contextual situation and preference in the market in ubiquitous environment. This system is based on collaborative filtering and Apriori algorithms. It will be able to provide customer-centric service to the customer, enhance shopping experiences and contribute in revitalizing market in the long term.

• Journal of Intelligence and Information Systems
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• v.18 no.3
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• pp.153-169
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• 2012

Exhibitions have played a key role of effective marketing activity which directly informs services and products to current and potential customers. Through participating in exhibitions, exhibitors have got the opportunity to make face-to-face contact so that they can secure the market share and improve their corporate images. According to this economic importance of exhibitions, show organizers try to adopt a new IT technology for improving their performance, and researchers have also studied services which can improve the satisfaction of visitors through analyzing visit patterns of visitors. Especially, as smart technologies make them monitor activities of visitors in real-time, they have considered booth recommender systems which infer preference of visitors and recommender proper service to them like on-line environment. However, while there are many studies which can improve their performance in the side of new technological development, they have not considered the choice factor of visitors for booth recommender systems. That is, studies for factors which can influence the development direction and effective diffusion of these systems are insufficient. Most of prior studies for the acceptance of new technologies and the continuous intention of use have adopted Technology Acceptance Model (TAM) and Extended Technology Acceptance Model (ETAM). Booth recommender systems may not be new technology because they are similar with commercial recommender systems such as book recommender systems, in the smart exhibition environment, they can be considered new technology. However, for considering the smart exhibition environment beyond TAM, measurements for the intention of reuse should focus on how booth recommender systems can provide correct information to visitors. In this study, through literature reviews, we draw factors which can influence the satisfaction and reuse intention of visitors for booth recommender systems, and design a model to forecast adaptation of visitors for booth recommendation in the exhibition environment. For these purposes, we conduct a survey for visitors who attended DMC Culture Open in November 2011 and experienced booth recommender systems using own smart phone, and examine hypothesis by regression analysis. As a result, factors which can influence the satisfaction of visitors for booth recommender systems are the effectiveness, perceived ease of use, argument quality, serendipity, and so on. Moreover, the satisfaction for booth recommender systems has a positive relationship with the development of reuse intention. For these results, we have some insights for booth recommender systems in the smart exhibition environment. First, this study gives shape to important factors which are considered when they establish strategies which induce visitors to consistently use booth recommender systems. Recently, although show organizers try to improve their performances using new IT technologies, their visitors have not felt the satisfaction from these efforts. At this point, this study can help them to provide services which can improve the satisfaction of visitors and make them last relationship with visitors. On the other hands, this study suggests that they managers along the using time of booth recommender systems. For example, in the early stage of the adoption, they should focus on the argument quality, perceived ease of use, and serendipity, so that improve the acceptance of booth recommender systems. After these stages, they should bridge the differences between expectation and perception for booth recommender systems, and lead continuous uses of visitors. However, this study has some limitations. We only use four factors which can influence the satisfaction of visitors. Therefore, we should development our model to consider important additional factors. And the exhibition in our experiments has small number of booths so that visitors may not need to booth recommender systems. In the future study, we will conduct experiments in the exhibition environment which has a larger scale.

• Journal of Information Technology Applications and Management
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• v.21 no.1
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• pp.85-105
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• 2014

Exhibition industry is important business domains to many countries. Not only lots of countries designated the exhibition industry as tools to stimulate national economics, but also many companies offer millions of service or products to customers. Recommender systems can help visitors navigate through large information spaces of various booths. However, no study before has proposed a methodology for identifying and acquiring prospective visitors although it is important to acquire them. Accordingly, we propose a methodology for identifying, acquiring prospective visitors, and recommending the adequate booth information to their preferences in the exhibition industry. We assume that a visitor will be interested in an exhibition within same class of exhibition taxonomy as exhibition which the visitor already saw. Moreover, we use user-based collaborative filtering in order to recommend personalized booths before exhibition. A prototype recommender system is implemented to evaluate the proposed methodology. Our experiments show that the proposed methodology is better than the item-based CF and have an effect on the choice of exhibition or exhibit booth through automation of word-of-mouth communication.