• Proceedings of the KAIS Fall Conference
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• 2003.11a
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• pp.113-117
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• 2003

As the wireless communication technology advances rapidly, a personalization technology can be incorporated with the mobile Internet environment, which is based on location-based services to support more accurate personalized services. A location-based personalized recommender system is one of the essential technologies of the location-based application services, and is also a crucial technology for the ubiquitous environment. In this paper we propose a framework of a location-based personalized recommender system for the mobile Internet environment. The proposed system consists of three modules the interface module, the neighbor selection module and the prediction and recommendation module. The proposed system incorporates the concept of the recommendation system in the Electronic Commerce along with that of the mobile devices for possible expansion of services on the mobile devices. Finally a service scenario for entertainment recommendation based on the proposed recommender system is described.

• Archives of design research
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• v.16 no.2
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• pp.23-34
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• 2003

The need for personalized web site design has been increased these days. Current approach for personalized web site design is easily applied to web site with their cost-effective feature, but is hard to provide a more refined personalized service due to its lack of accumulation of user data. In this study, the design recommender system is investigated as a more advanced method for web site design personalization. We provide an overview of current recommender systems, and then outlined a newly developed design recommender system, which employs collaborative filtering technique to provide tailored recommendation for users.

• IEMEK Journal of Embedded Systems and Applications
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• v.4 no.3
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• pp.139-147
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• 2009

This paper proposed the context awareness reasoning system to provide the personalized services dynamically in a ubiquitous mobile environments. The proposed system is designed to provide the personalized services to mobile users and consists of the context aggregator and the knowledge manager. The context aggregator can collect information from networks through Open API Gateway as well as sensors in a various ubiquitous environment. And it can also extract the place types through the geocoding and the social address domain ontology. The knowledge manager is the core component to provide the personalized services, and consists of activity reasoner, user pattern learner and service recommender to provide the services predict by extracting the optimized service from user situations. Activity reasoner uses the ontology reasoning and user pattern learner learns with previous service usage history and contexts. And to design service recommender easy to flexibly apply in dynamic environments, service recommender recommends service in the only use of current accessible contexts. Finally, we evaluate the learner and recommender of proposed system by simulation.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2008.10a
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• pp.410-414
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• 2008

Providing recommendations is acknowledged as one of important features of a business-to-consumer online storefront. Though there have been many studies on algorithms and operational procedures of personalized recommender systems, there is still a lack of empirical evidence demonstrating relationships between social presence and two important outcome variables of recommender systems: reuse intention and trust. To test the existence of a causal link between social presence and reuse intention, and mediating role of trust between these two variables, this study performed experiments varying level of social presence while providing personalized recommendations to users based on their explicit preferences. This study also compared these effects in two different product contexts: hedonic and utilitarian product. The results show that the provision of higher social presence increases both the reuse intention and trust of the recommender systems. In addition, the influence of social presence on reuse intention in the setting of recommending utilitarian products is less than that in the setting of recommending hedonic products.

• The Journal of Society for e-Business Studies
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• v.17 no.3
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• pp.85-103
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• 2012

This study focused on user evaluation for personalized recommender systems with the integrated view of performance of the system and user attitude of recommender systems. Since users' evaluations of recommender systems can be affected by recommendation outcomes and presentation methods, both system performances based on outcomes and user attitudes formed by the presentation methods should be considered when explaining users' evaluations. However, an integrated view of system performance and user attitudes has not been applied to explain users' evaluation of recommender systems. Thus, the goal of this study is to explain users' evaluations of recommender systems under the integrated view of predictive features and explanation features at the same time. Our findings suggest that social presence, both accuracy and noveltyhave impacts onuser satisfaction for recommender systems. Especially, predictive features including accuracy and novelty affected user satisfaction. Novelty as well as accuracy is one of the significant factors for user satisfaction while recommender systems provided usual items users have experienced when systems provide serendipitous items. Likewise, explanation features with social presence and self-reference were important for user evaluation of personalized recommender systems. For explanation features, while social presence appears as one of important factors to user satisfaction of evaluating personalized recommendations, self-reference has no significant effect on user's satisfaction for recommender systems when compared to the result of social presence. Self-referencing messages did not affect user satisfaction but the levels of self-referencing are different between low and high groups in the experiment.

• Journal of the Korea Society for Simulation
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• v.19 no.4
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• pp.59-66
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• 2010

We present a personalized clothing recommender system - one that mines association rules from transaction described in ontologies and infers a recommendation from the rules. The recommender system can forecast frequently changing trends of clothing using the Onto-Apriori algorithm, and it makes appropriate recommendations for each users possible through the inference marked as meta nodes. We simulates the rule generator and the inferential search engine of the system with focus on accuracy and efficiency, and our results validate the system.

• Journal of Intelligence and Information Systems
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• v.17 no.3
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• pp.115-130
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• 2011

Many online stores bring features that can build trust in their customers. More so, the number of products or content services on online stores has been increasing rapidly. Hence, personalization on online stores is considered to be an important technology to companies and customers. Recommender systems that provide favorable products and customer product reviews to users are the most commonly used features in this purpose. There are many studies to that investigated the relationship between social presence as an antecedent of trust and provision of recommender systems or customer product reviews. Many online stores have made efforts to increase perceived social presence of their customers through customer reviews, recommender systems, and analyzing associations among products. Primarily because social presence can increase customer trust or reuse intention for online stores. However, there were few studies that investigated the interactions between recommendation type, product type and provision of customer product reviews on social presence. Therefore, one of the purposes of this study is to identify the effects of personalized recommender systems and compare the role of customer reviews with product types. This study performed an experiment to see these interactions. Experimental web pages were developed with $2{\times}2$ factorial setting based on how to provide social presence to users with customer reviews and two product types such as hedonic and utilitarian. The hedonic type was a ringtone chosen from Nate.com while the utilitarian was a TOEIC study aid book selected from Yes24.com. To conduct the experiment, web based experiments were conducted for the participants who have been shopping on the online stores. Participants were a total of 240 and 30% of the participants had the chance of getting the presents. We found out that social presence increased for hedonic products when personalized recommendations were given compared to non.personalized recommendations. Although providing customer reviews for two product types did not significantly increase social presence, provision of customer product reviews for hedonic (ringtone) increased perceived social presence. Otherwise, provision of customer product reviews could not increase social presence when the systems recommend utilitarian products (TOEIC study.aid books). Therefore, it appears that the effects of increasing perceived social presence with customer reviews have a difference for product types. In short, the role of customer reviews could be different based on which product types were considered by customers when they are making a decision related to purchasing on the online stores. Additionally, there were no differences for increasing perceived social presence when providing customer reviews. Our participants might have focused on how recommendations had been provided and what products were recommended because our developed systems were providing recommendations after participants rating their preferences. Thus, the effects of customer reviews could appear more clearly if our participants had actual purchase opportunity for the recommendations. Personalized recommender systems can increase social presence of customers more than nonpersonalized recommender systems by using user preference. Online stores could find out how they can increase perceived social presence and satisfaction of their customers when customers want to find the proper products with recommender systems and customer reviews. In addition, the role of customer reviews of the personalized recommendations can be different based on types of the recommended products. Even if this study conducted two product types such as hedonic and utilitarian, the results revealed that customer reviews for hedonic increased social presence of customers more than customer reviews for utilitarian. Thus, online stores need to consider the role of providing customer reviews with highly personalized information based on their product types when they develop the personalized recommender systems.

• Journal of Internet Computing and Services
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• v.17 no.5
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• pp.75-87
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• 2016

With the exploration of digital academic information, it is certainly required to develop more effective academic contents recommender system in order to accommodate increasing needs for accessing more personalized academic contents. Considering historical usage data, the academic content recommender system recommends personalized academic contents which corresponds with each user's preference. So, the academic content recommender system effectively increases not only the accessibility but also usability of digital academic contents. In this paper, we propose the new journal recommendation technique based on information of journal subscription and web usage logs in order to properly recommend more personalized academic contents. Our proposed recommendation method predicts user's preference with the institution similarity, the journal similarity and journal importance based on citation relationship data of references and finally compose institute-oriented recommendations. Also, we develop a recommender system prototype. Our developed recommender system efficiently collects usage logs from distributed web sites and processes collected data which are proper to be used in proposed recommender technique. We conduct compare performance analysis between existing recommender techniques. Through the performance analysis, we know that our proposed technique is superior to existing recommender methods.

• Journal of KIISE:Databases
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• v.41 no.4
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• pp.209-216
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• 2014

Various characteristics of social network contents such as real-time, people relationship and big data can help to improve personalized recommender systems. Among them, 'people relationship' is a key factor of recommendation, so many personalized recommender systems utilizing it have been proposed. However, existing researches can not reflect personal tendency and are unable to provide precise recommendations in various domains, because they do not consider intimacy among people. In this paper, to solve these problems, we propose PReAmacy, a Personalized Recommendation Algorithm, considering intimacy among users and various characteristics of social network contents. Our experimental results indicate that not only the precision of PReAmacy is higher than that of existing algorithms, but intimacy is of great importance in PReAmacy.

• Asia pacific journal of information systems
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• v.15 no.1
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• pp.63-79
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• 2005

Recommender systems are a personalized information filtering technology to help customers find the products they would like to purchase. Collaborative filtering is known to be the most successful recommendation technology, but its widespread use has exposed some problems such as sparsity and scalability in the e-business environment. In this paper, we propose a recommendation system, WebCF-PT based on Web usage mining and product taxonomy to enhance the recommendation quality and the system performance of traditional CF-based recommender systems. Web usage mining populates the rating database by tracking customers' shopping behaviors on the Web, so leading to better quality recommendations. The product taxonomy is used to improve the performance of searching for nearest neighbors through dimensionality reduction of the rating database. A prototype recommendation system, WebCF-PT is developed and Internet shopping mall, EBIB(e-Business & Intelligence Business) is constructed to test the WebCF-PT system.