• Asia pacific journal of information systems
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• 제34권1호
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• pp.226-247
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• 2024

Most studies on recommender systems to evaluate recommendation performances focus on offline evaluation methods utilizing past customer transaction records. However, evaluating recommendation performance through real-world stimulation becomes challenging. Moreover, such methods cannot evaluate the duration of the recommendation effect. This study measures the personalized recommendation (stimulus) effect when the product recommendation to customers leads to actual purchases and evaluates the duration of the stimulus personalized recommendation effect leading to purchases. The results revealed a 4.58% improvement in recommendation performance in the online environment compared with that in the offline environment. Furthermore, there is little difference in recommendation performance in offline experiments by period, whereas the recommendation performance declines with time in online experiments.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권11호
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• pp.3050-3063
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• 2023

Recommendation systems provide personalized products or services to online users by mining their past preferences. Collaborative filtering is a popular recommendation technique because it is easy to implement. However, with the rapid growth of the number of users in recommendation systems, collaborative filtering suffers from serious scalability and sparsity problems. To address these problems, a novel collaborative filtering recommendation algorithm is proposed. The proposed algorithm partitions the users using affinity propagation clustering, and searches for k nearest neighbors in the partition where active user belongs, which can reduce the range of searching and improve real-time performance. When predicting the ratings of active user's unrated items, mean deviation method is used to impute values for neighbors' missing ratings, thus the sparsity can be decreased and the recommendation quality can be ensured. Experiments based on two different datasets show that the proposed algorithm is excellent both in terms of real-time performance and recommendation quality.

• 디지털산업정보학회논문지
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• 제9권1호
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• pp.43-52
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• 2013

Pervasive computing and social network are good resources in recommendation method. Collaborative filtering is one of the most popular recommendation methods, but it has some limitations such as rating sparsity. Moreover, it does not consider social network in pervasive computing environment. We propose an effective proactive friend recommendation method using social network and contexts in pervasive computing environment. In collaborative filtering method, users need to rate sufficient number of items. However, many users don't rate items sufficiently, because the rating information must be manually input into system. We solve the rating sparsity problem in the collaboration filtering method by using contexts. Our method considers both a static and a dynamic friendship using contexts and social network. It makes more effective recommendation. This paper describes a new friend recommendation method and then presents a music friend scenario. Our work will help e-commerce recommendation system using collaborative filtering and friend recommendation applications in social network services.

• Asia pacific journal of information systems
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• 제29권4호
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• pp.752-770
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• 2019

The purpose of this study is to incorporate telemarketing processes to improve telemarketing performance. For this application, we have attempted to mix the model of machine learning to extract potential customers with personalisation techniques to derive recommended products from actual contact. Most of traditional recommendation systems were mainly in ways such as collaborative filtering, which predicts items with a high likelihood of future purchase, based on existing purchase transactions or preferences for products. But, under these systems, new users or items added to the system do not have sufficient information, and generally cause problems such as a cold start that can not obtain satisfactory recommendation items. Also, indiscriminate telemarketing attempts can backfire as they increase the dissatisfaction and fatigue of customers who do not want to be contacted. To this purpose, this study presented a multi-purpose hybrid recommendation algorithm to achieve two goals: to select customers with high possibility of contact, and to recommend products to selected customers. In addition, we used subscription data from telemarketing agency that handles insurance products to derive realistic applicability of the proposed recommendation system. Our proposed recommendation system would certainly solve the cold start and scarcity problem of existing recommendation algorithm by using contents information such as customer master information and telemarketing history. Also. the model could show excellent performance not only in terms of overall performance but also in terms of the recommendation success rate of the unpopular product.

• 산업경영시스템학회지
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• 제46권3호
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• pp.78-88
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• 2023

The increasing number of technology transfers from public research institutes in Korea has led to a growing demand for patent recommendation platforms for SMEs. This is because selecting the right technology for commercialization is a critical factor in business success. This study developed a patent recommendation system that uses technology transfer data from the past 10 years to recommend patents that are suitable for SMEs. The system was developed in three stages. First, an item-based collaborative filtering system was developed to recommend patents based on the similarities between the patents that SMEs have previously transferred. Next, a content-based recommendation system based on TF-IDF was developed to analyze patent names and recommend patents with high similarity. Finally, a hybrid system was developed that combines the strengths of both recommendation systems. The experimental results showed that the hybrid system was able to recommend patents that were both similar and relevant to the SMEs' interests. This suggests that the system can be a valuable tool for SMEs that are looking to acquire new technologies.

• 한국경영과학회지
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• 제35권2호
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• pp.37-51
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• 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.

• 대한안전경영과학회지
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• 제13권2호
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• pp.169-177
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• 2011

In this paper, the author devised new decision recommendation ordering method of items attributed by age to improve accuracy of recommender system. In conventional recommendation system, recommendation order is decided by high order of preference prediction. However, in this paper, recommendation accuracy is improved by decision recommendation order method that reflect age attribute of target customer and neighborhood in preference prediction. By applying decision recommendation order method to recommender system, recommendation accuracy is improved more than conventional ordering method of recommendation.

• 디지털산업정보학회논문지
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• 제5권2호
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• pp.95-100
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• 2009

The Semantic Web is an evolving extension of the WWW in which the semantics of information and services on the web is defined, making it possible for the web to understand and satisfy the requests of people and machines to use the web content. The sementic web relied on the ontologies that structure underling data for the purpose of comprehensive and transportable machine understanding. The Semantic Web relies on the ontologies that structure underlying data for the purpose of comprehensive and transportable machine understanding. And recommendation systems have been developed as a solution to the abundance of choice people face in many situations. This paper shows that the new recommendation method is suitable for effective recommendation on the semantic web. We present a new procedure for improving the effective recommendation by using the levelized context ontology. Our experimental results also confirm that our method has good recommendation time. Our proposed method can be generalized to fit other application domains.

• Asia pacific journal of information systems
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• 제29권2호
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• pp.203-216
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• 2019

There is an accuracy-diversity dilemma with personalized recommendation services. Some researchers believe that accurate recommendations might reinforce customer satisfaction. However, others claim that highly accurate recommendations and customer satisfaction are not always correlated. Thus, this study attempts to establish the causal factors that determine customer satisfaction with personalized recommendation services to reconcile these incompatible views. This paper employs statistical analyses of simulation to investigate an accuracy-diversity dilemma with personalized recommendation services. To this end, we develop a personalized recommendation system and measured accuracy, diversity, and customer satisfaction using a simulation method. The results show that accurate recommendations positively affected customer satisfaction, whereas diverse recommendations negatively affected customer satisfaction. Also, customer satisfaction was associated with the recommendation product size when neighborhood size was optimal in accuracy. Thus, these results offer insights into personalizing recommendation service providers. The providers must identify customers' preferences correctly and suggest more accurate recommendations. Furthermore, accuracy is not always improved as the number of product recommendation increases. Accordingly, providers must propose adequate number of product recommendation.

• 지능정보연구
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• 제16권4호
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• pp.195-211
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• 2010

전시회는 전시업체가 새로운 상품이나 서비스를 관람객에게 알리기 위해 개최되는 것으로 효과적인 마케팅 수단으로 중요한 역할을 수행한다. 전시회를 방문하는 다양한 관람객의 니즈를 충족시키기 위하여 다양한 유비쿼터스 기술이 전시회에 응용되고 있지만 관람객이 사전에 요청한 정보만을 제공함으로 개별 관람객의 선호가 반영되지 않아 관람객의 니즈를 충족시키기에는 한계가 있다. 이러한 한계를 해결하기 위한 방법으로 개인의 선호에 부합하는 부스를 추천하는 추천 시스템의 이용이 가능하다. 추천시스템은 전시 환경에서 관람객의 선호를 추론하여 선호에 부합하는 방문 부스를 추천하여 관람객의 니즈를 충족시킬 수 있다. 그러나 추천 시스템 중 가장 성공적으로 평가 받는 기존의 협업 필터링은 관람객의 부스 방문 순서에 나타나는 선호를 반영하지 않아 동적으로 변화하는 선호를 가지는 관람객으로 구성된 전시 환경의 추천 시스템으로는 적합하지 않다. 따라서 본 연구에서는 관람객의 방문 순서를 고려하는 기법 중 순차 연관 규칙을 이용하여 관람객의 선호에 부합하는 부스를 추천하는 방법론을 제안하였다. 본 연구에서 제안한 방법론의 성과 측정을 위해 실제 전시회에서 획득한 데이터를 사용하여 기존의 협업 필터링과 비교한 결과 전체적으로 추천의 성과가 향상되어 향후 전시 환경에서의 부스 추천시스템에 적용하여 관람객의 니즈를 충족시킬 것으로 기대된다.