• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제3권4호
• /
• pp.366-375
• /
• 2009

In collaborative filtering, many neighbors are needed to improve the quality and stability of the recommendation. The quality may not be good mainly due to the high similarity between two users not guaranteeing the same preference for products considered for recommendation. This paper proposes a consistency definition, rather than similarity, based on information entropy between two users to improve the recommendation. This kind of consistency between two users is then employed as a trust metric in collaborative filtering methods that select neighbors based on the metric. Empirical studies show that such collaborative filtering reduces the number of neighbors required to make the recommendation quality stable. Recommendation quality is also significantly improved.

• 디지털산업정보학회논문지
• /
• 제13권1호
• /
• pp.23-35
• /
• 2017

With the popularization of mobile devices, the number of social network service users is increasing, thereby the amount of data is also increasing accordingly. As Internet of Things environment is expanding to connect things and people, there is information much more than before. In such an environment, it becomes very important to recommend the necessary information to the user. In this paper, we propose a recommendation method that considers new users in IoT environment. In the proposed method, we recommend the information by applying the centrality-based social network analysis method to the recommendation method using the social relationships in the social IoT. We describe the seven-step recommendation method and apply them to the music circle scenario of the IoT environment. Through the music circle scenario, we show that we can recommend more suitable information to new users in the IoT environment than the existing recommendation method.

• 한국경영과학회지
• /
• 제35권2호
• /
• pp.37-51
• /
• 2010

Understanding the antecedents of high public usage of national R&D facilities is a critical issue for both academics and facility managers. Previous researchrelated to general service management has identified service quality and user satisfaction as important antecedents of reuse and recommendation intention. The current paper reports findings from a survey which looked into the impact of service quality dimensions and user satisfaction on reuse and recommendation intention in the field of R&D facility public usage. Findings indicate that service quality appears to be linked to user satisfaction, and user satisfaction to be linked to reuse and recommendation intention. Findings also indicate that user satisfaction played as a mediator on the relationship between service quality and reuse/recommendation intentions in R&D facility public usage domain.

• 한국지능정보시스템학회:학술대회논문집
• /
• 한국지능정보시스템학회 2007년도 한국지능정보시스템학회
• /
• pp.218-225
• /
• 2007

Recommendation system is one way of implementing personalized service. The collaborative filtering is one of the major techniques that have been employed for recommendation systems. It has proven its effectiveness in the recommendation systems for such domain as motion picture or music. However, it has some limitations, i.e., sparsity and scalability. In this research, as one way of overcoming such limitations, we proposed the stepwise collaborative filtering method. To show the practicality of our proposed method, we designed and implemented a movie recommendation system which we shall call Step_CF, and its performance was evaluated using MovieLens data. The performance of Step_CF was better than that of Basic_CF that was implemented using the original collaborative filtering method.

• 한국IT서비스학회지
• /
• 제13권2호
• /
• pp.173-184
• /
• 2014

As social network services has become one of the most successful web-based business, recommendation in social network sites that assist people to choose various products and services is also widely adopted. Collaborative Filtering is one of the most widely adopted recommendation approaches, but recommendation technique that use explicit or implicit social network information from social networks has become proposed in recent research works. In this paper, we reviewed and compared research works about recommendation using social network analysis and collaborative filtering in social network sites. As the results of the analysis, we suggested the trends and implications for future research of recommendation in SNSs. It is expected that graph-based analysis on the semantic social network and systematic comparative analysis on the performances of social filtering and collaborative filtering are required.

• Journal of Multimedia Information System
• /
• 제4권4호
• /
• pp.279-284
• /
• 2017

The visual impact of fonts on lots of documents and design work is significant. Accordingly, the users desire to appropriately use fonts suitable for their intention. However, existing font recommendation programs are difficult to consider what users want. Therefore, we propose a font recommendation system based on user-evaluated font attribute value. The properties of a font are called attributes. In this paper, we propose a font recommendation module that recommends a user 's desired font using the attributes of the font. In addition, we classify each attribute into three types of usage, personality, and shape, suggesting the font that is closest to the desired font, and suggest an optimal font recommendation algorithm. In addition, weights can be set for each use, personality, and shape category to increase the weight of each category, and when a weight is used, a more suitable font can be recommended to the user.

• Asia pacific journal of information systems
• /
• 제14권2호
• /
• pp.131-149
• /
• 2004

Collaborative filtering is one of popular techniques for personalized recommendation in e-commerce sites. An advantage of collaborative filtering is that the technique can work with sparse evaluation data to predict preference scores of new alternative contents or advertisements. There is, however, no in-depth study about the sparsity effect of customer's evaluation data to the performance of recommendation. In this study, we investigate the sparsity effect and hybrid usages of customers' evaluation data and purchase data using an experiment result. The result of the analysis shows that the performance of recommendation decreases monotonically as the sparsity increases, and also the hybrid usage of two different types of data; customers' evaluation data and purchase data helps to increase the performance of recommendation in sparsity situation.

• 한국IT서비스학회지
• /
• 제6권2호
• /
• pp.113-123
• /
• 2007

In ubiquitous computing environment such as ubiquitous marketplace (u-market), there is a need of providing context-based personalization service while considering the nomadic user preference and corresponding requirements. To do so, the recommendation systems should deal with the tremendous amount of context data. Hence, the purpose of this paper is to propose a novel recommendation method which provides the products-group list of the customers in u-market based on the shopping intention and preferences. We have developed FREPIRS(FREquent Purchased Item-sets Recommendation Service), which makes recommendation listof product-group, not individual product. Collaborative filtering and apriori algorithm are adopted in FREPIRS to build product-group.

• 한국콘텐츠학회논문지
• /
• 제6권5호
• /
• pp.1-8
• /
• 2006

유비쿼터스 환경 하에서 다중 상황 기반 권유 서비스에 대한 요구가 증대하고 있다. 이러한 환경에서는 상황의 수가 증가함에 따라 권유 정보의 양이 크게 증가하게 되어 효과적인 정보 제공이 어려워진다는 문제를 가진다. 이를 위해 본 논문에서는 유비쿼터스 환경에서 다중 상황 적응적인 효과적인 권유 기법을 제안한다. 본 제안 기법에서는 상황별로 의미 있는 정보를 제공할 수 있도록 하기 위해 사용자들의 상황별 선호도와 행위를 권유 정보의 양을 결정하는 가중치 요소로서 사용한다. 이를 위해 권유 기법과 시나리오를 제시하고, 본 논문에서 제안하는 기법의 효과성을 실험을 통해 평가한다.

• 한국지능정보시스템학회:학술대회논문집
• /
• 한국지능정보시스템학회 2003년도 춘계학술대회
• /
• pp.400-409
• /
• 2003

Collaborative filtering (CF) recommendation is a knowledge sharing technology for distribution of opinions and facilitating contacts in network society between people with similar interests. The main concerns of the CF algorithm are about prediction accuracy, speed of response time, problem of data sparsity, and scalability. In general, the efforts of improving prediction algorithms and lessening response time are decoupled. We propose a three-step CF recommendation model which is composed of profiling, inferring, and predicting steps while considering prediction accuracy and computing speed simultaneously. This model combines a CF algorithm with two machine learning processes, SOM (Self-Organizing Map) and CBR (Case Based Reasoning) by changing an unsupervised clustering problem into a supervised user preference reasoning problem, which is a novel approach for the CF recommendation field. This paper demonstrates the utility of the CF recommendation based on SOM cluster-indexing CBR with validation against control algorithms through an open dataset of user preference.