• Journal of Korea Society of Digital Industry and Information Management
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• v.8 no.4
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• pp.43-51
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• 2012

Web 3.0 is the next-generation of the World Wide Web and is included two main platforms, semantic technologies and social computing environment. The basic idea of web 3.0 is to define structure data and link them in order to more effective discovery, automation, integration, and reuse across various applications. The semantic technologies represent open standards that can be applied on the top of the web. The social computing environment allows human-machine co-operations and organizing a large number of the social web communities. In the recent years, recommender systems have been combined with ontologies to further improve the recommendation by adding semantics to the context on the web 3.0. In this paper, we study previous researches about recommendation method and propose a recommendation method based on web 3.0. Our method scores documents based on context tags and social network services. Our social scoring model is computed by both a tagging score of a document and a tagging score of a document that was tagged by a user's friends.

• Journal of the Korean Society for information Management
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• v.26 no.1
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• pp.81-105
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• 2009

In this paper, we propose user contents using behavior and location information on contents on various channels, such as web, IPTV, for contents distribution. With methods to build user and contents profiles, contents using behavior as an implicit user feedback was applied into machine learning procedure for updating user profiles and contents preference. In machine learning procedure, contents-based and collaborative filtering methods were used to analyze user's contents preference. This study proposes contents location information on web sites for final recommendation contents as well. Finally, we refer to a generalized recommender system for personalization. With those methods, more effective and accurate recommendation service can be possible.

• Asia pacific journal of information systems
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• v.16 no.3
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• pp.159-176
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• 2006

Collaborative filtering is one of the popular techniques for personalized recommendation in e-commerce. In collaborative filtering, user profiles are usually managed per product category in order to reduce data sparsity. Product diversification of Internet storefronts and multiple product category sales of e-commerce portals require cross-product category usage of user profiles in order to overcome the cold start problem of collaborative filtering. In this paper, we study the feasibility of cross-product category usage of user profiles, and suggest a method to improve recommendation performance of cross-product category user profiling. First, we investigate whether user profiles on a product category can be used to recommend products in other product categories. Furthermore, a way of utilizing user profiles selectively is suggested to increase recommendation performance of cross-product category user profiling. The feasibility of cross-product category user profiling and the usefulness of the proposed method are tested with real click stream data of an Internet storefront which sells multiple product categories including books, music CDs, and DVDs. The experiment results show that user profiles on a product category can be used to recommend products in other product categories. Also, the selective usage of user profiles based on correlations between subcategories of two product categories provides better performance than the whole usage of user profiles.

• The Korean Journal of Franchise Management
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• v.14 no.2
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• pp.1-17
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• 2023

Purpose: Due to the COVID-19 and inflation, participation sports companies, including fitness centers, are facing challenges. Since a fitness center must simultaneously manage facilities and operate services, both factors must be considered when developing a marketing strategy. Therefore, this study examines the effects of expanded marketing mix factors (price, physical evidence, place, people, product, and promotion) including facilities and services on the consumption behavior (satisfaction, recommendation intention, repurchase intention) of fitness center customers. Research design, data, and methodology: The data were collected from sample of 323 fitness club members in Seoul and analyzed with SPSS Win Ver.28.0 program. Result: The specific results of the study were as follows; First, extended marketing mix factors had significant positive (+) effect on satisfaction. Second, extended marketing mix factors had significant positive (+) effect on recommendation intention. Third, extended marketing mix factors had significant positive (+) effect on repurchase intention. Fourth, satisfaction had significant positive (+) effect on recommendation intention and repurchase intention. Conclusions: To encourage consumption behavior, it is necessary to convert existing customers into loyal ones by increasing satisfaction and establishing a virtuous cycle structure that recommends them to others while also improving repurchase intention.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.6
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• pp.552-562
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• 2020

This study examined the effects of apartment complex management and residential quality on the residents' residential satisfaction and recommendation intentions. A survey was conducted on the residents of an apartment complex in Doan, Daejeon. Statistical analysis was analyzed using the SPSS 25.0 program. Exploratory factor analysis (EFA) was carried out to verify the validity of the measurement tools for apartment complex management, residential quality, living satisfaction, and recommendation intention. The Cronbach's α coefficient was evaluated to verify the reliability of the measurement tools. Multi-regression analyses were conducted to verify the research hypotheses. As a result, the following main results were derived. First, maintenance factors and living management factors among apartment complex management factors were found to have a significant effect on the residents' residential satisfaction. Second, among the factors of residential quality in apartment complexes, convenience, safety, comfortability, and economy had a significant effect on residential satisfaction. Third, residential satisfaction had a significant effect on the recommendation intention. Overall, the factors of apartment complex management and residential quality affecting residential satisfaction and recommendation intentions were derived.

• Asia pacific journal of information systems
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• v.17 no.1
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• pp.123-145
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• 2007

There are flourishing studies in the acceptance or usage of information systems literature. Most of them have taken the pro - acceptance view. Undesirably, information technologies often provoke users' reactance or resistance. This paper explores one of the negative reactions -psychological reactance. The present paper studies the effects of the users' perception of threatened freedom and personalization degree on intention to use recommendation services. High personalization can be a major motivation for users to accept recommendation systems. However recommendation services are a two-edged sword, which not only provides users the efficiency of decision making but also poses threats to free choice. When people consider that their freedom is reduced or threatened by others, they experience the motivational state to restore the freedom. This motivational state must be considered in understanding usage of information systems, especially personalized services which are designed for persuasion or compliance. This paper empirically investigates the effect of personalization and the psychological reactance on the intention to use information systems in the personalized recommendation context. Users' perception of personalization increases the usefulness of recommendation service while their perception of threat to freedom reduces the intention to use personalized recommendation service. Findings and implications are discussed.

• Journal of the Korean Society for information Management
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• v.39 no.3
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• pp.1-22
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• 2022

The purpose of this study is to propose a book recommendation system that can be used in school libraries. The book recommendation system applies an algorithm based on association rules using DLS lending data and is designed to provide personalized book recommendation services to school library users. For this purpose, association rules based on the Apriori algorithm and betweenness centrality analysis were applied and detailed functions such as descriptive statistics, generation of association rules, student-centered recommendation, and book-centered recommendation were materialized. Subsequently, opinions on the use of the book recommendation system were investigated through in-depth interviews with teacher librarians. As a result of the investigation, opinions on the necessity and difficulty of book recommendation, student responses, differences from existing recommendation methods, utilization methods, and improvements were confirmed and based on this, the following discussions were proposed. First, it is necessary to provide long-term lending data to understand the characteristics of each school. Second, it is necessary to discuss the data integration plan by region or school characteristics. Third, It is necessary to establish a book recommendation system provided by the Comprehensive Support System for Reading Education. Based on the contents proposed in this study, it is expected that various discussions will be made on the application of a personalization recommendation system that can be used in the school library in the future.

• Journal of the Korea Convergence Society
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• v.11 no.11
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• pp.19-24
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• 2020

Recently, due to the rapid increase of pets, there is a need for an integrated pet-related personalized product recommendation service such as feed recommendation using a health status check of pets and various collected data. This paper implements a product recommendation system that can perform various personalized services such as collection, pre-processing, analysis, and management of pet-related data using big data. First, the sensor information worn by pets, customer purchase patterns, and SNS information are collected and stored in a database, and a platform capable of customized personalized recommendation services such as feed production and pet health management is implemented using statistical analysis. The platform can provide information to customers by outputting similarity product information about the product to be analyzed and information, and finally outputting the result of recommendation analysis.

• Journal of Internet Computing and Services
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• v.15 no.5
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• pp.73-82
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• 2014

SNS becomes a recent issue, and many researches in various kinds of field are being done by taking advantage of it. Especially, there are many researches existed on the system that finds user's interest and makes recommendation based on multiple social data generated on the SNS. User's interest is not only revealed from the user's writing but also from the user's relationship with friends. This study proposes a recommendation method that extracts user's interest by using social relationship and its categorization applies it to the recommendation. In this way, it can recommend user's interest with category based on the writings by the user and furthermore it can apply the user's relationship with his/her friends for more accurate recommendation. In addition, if necessary, the recommendation can be made by extracting any interest shared between the user and specific friends. Through experiments, we show that our method using social category can produce satisfactory result.

• Journal of the East Asian Society of Dietary Life
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• v.20 no.3
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• pp.389-395
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• 2010

This research was focused on observing the effect of Makgeolli's selection attributes on customer satisfaction, recommendation intention, and repurchase intention. The purpose of this study was to examine to present a marketing-related suggestion by finding the components that needs to be discussed in order to satisfy the customer and lead to positive word of mouth and repurchasing in the perspective of a corporation. The evidence to achieve the research purpose can be summarized as below. To begin with, the causes of Makgeolli's selection attributes were classified into 9 types, which are design and ad image, expertise and tradition, drinking experience and in harmony with food, taste and freshness, materials and origin, brand image, flavor and color, alcoholic and nutrition, and finally price and recommendation. And it showed up that the average importance of the taste and freshness is the highest. Moreover, the study on the Makgeolli's state of being potable showed up that the drinking number was no more than once a month, and one drink was almost all less than a bottle. The drinking place was usually tavern, and word of mouth was the most often used information medium that contacted Makgeolli. The potential of the Makgeolli's globalization is 80.6% which added positive and very positive, that enables us to infer that the Makgeolli's global dependency is very high. Third, from the 9 types of classification mentioned before, taste and freshness, and price and recommendation were proved to be influential in satisfaction, and recommendation is affecting the repurchase intention and the recommendation intention.