• International Journal of Fuzzy Logic and Intelligent Systems
• /
• 제7권2호
• /
• pp.91-95
• /
• 2007

Main goal of web recommendation system is to study how user behavior on a website can be predicted by analyzing web log data which contain the visited web pages. Many researches of the web recommendation system have been studied. To construct web recommendation system, web mining is needed. Especially, web usage analysis of web mining is a tool for recommendation model. In this paper, we propose web recommendation system using grid based support vector machines for improvement of web recommendation system. To verify the performance of our system, we make experiments using the data set from our web server.

• 디지털산업정보학회논문지
• /
• 제5권2호
• /
• pp.95-100
• /
• 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
• /
• 제15권1호
• /
• pp.63-79
• /
• 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.

• Journal of Information Processing Systems
• /
• 제17권2호
• /
• pp.426-439
• /
• 2021

To alleviate the cold-start problem and data sparsity in web service recommendation and meet the personalized needs of users, this paper proposes a personalized web service recommendation method based on a hybrid social network and multi-objective immune optimization. The network adds the element of the service provider, which can provide more real information and help alleviate the cold-start problem. Then, according to the proposed service recommendation framework, multi-objective immune optimization is used to fuse multiple attributes and provide personalized web services for users without adjusting any weight coefficients. Experiments were conducted on real data sets, and the results show that the proposed method has high accuracy and a low recall rate, which is helpful to improving personalized recommendation.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제17권10호
• /
• pp.2809-2821
• /
• 2023

Effective recommendation of similar business groups is a critical factor in obtaining market information for companies. In this study, we propose a novel method for enhancing similar business group recommendation by incorporating derivative criteria and web crawling. We use employment announcements, employment incentives, and corporate vocational training information to derive additional criteria for similar business group selection. Web crawling is employed to collect data related to the derived criteria from 'credit jobs' and 'worknet' sites. We compare the efficiency of different datasets and machine learning methods, including XGBoost, LGBM, Adaboost, Linear Regression, K-NN, and SVM. The proposed model extracts derivatives that reflect the financial and scale characteristics of the company, which are then incorporated into a new set of recommendation criteria. Similar business groups are selected using a Euclidean distance-based model. Our experimental results show that the proposed method improves the accuracy of similar business group recommendation. Overall, this study demonstrates the potential of incorporating derivative criteria and web crawling to enhance similar business group recommendation and obtain market information more efficiently.

• 한국지능시스템학회논문지
• /
• 제13권3호
• /
• pp.366-370
• /
• 2003

In this research, we suggest a hybrid intelligent Web recommendation systems based on Web data mining and case-based reasoning (CBR). One of the important research topics in the field of Internet business is blending artificial intelligence (AI) techniques with knowledge discovering in database (KDD) or data mining (DM). Data mining is used as an efficient mechanism in reasoning for association knowledge between goods and customers＇ preference. In the field of data mining, the features, called attributes, are often selected primary for mining the association knowledge between related products. Therefore, most of researches, in the arena of Web data mining, used association rules extraction mechanism. However, association rules extraction mechanism has a potential limitation in flexibility of reasoning. If there are some goods, which were not retrieved by association rules-based reasoning, we can＇t present more information to customer. To overcome this limitation case, we combined CBR with Web data mining. CBR is one of the AI techniques and used in problems for which it is difficult to solve with logical (association) rules. A Web-log data gathered in real-world Web shopping mall was given to illustrate the quality of the proposed hybrid recommendation mechanism. This Web shopping mall deals with remote-controlled plastic models such as remote-controlled car, yacht, airplane, and helicopter. The experimental results showed that our hybrid recommendation mechanism could reflect both association knowledge and implicit human knowledge extracted from cases in Web databases.

• 한국산학기술학회논문지
• /
• 제14권11호
• /
• pp.5856-5865
• /
• 2013

모바일앱의 플랫폼 종속성과 디바이스 자원 한계에 대한 이슈를 극복하기 위해 모바일웹 서비스에 대한 요구가 증가하고 있다. 이러한 모바일웹 서비스의 개발 및 운영 생산성을 향상시키기 위한 방법은 모바일웹들 간에 낮은 결합력을 제공하는 것이다. 본 논문에서는 결합력을 낮추기 위해 모바일웹 동적 연결의 추천기법을 제안한다. 모바일웹 추천기법은 XDPL 기반으로 제안한다.

• 디지털산업정보학회논문지
• /
• 제8권4호
• /
• pp.43-51
• /
• 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.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제15권5호
• /
• pp.1708-1727
• /
• 2021

In the advent of the twenty-first century, human beings began to closely interact with technology. Today, technology is developing, and as a result, the world wide web (www) has a very important place on the Internet and the significant task is fulfilled by Web services. A lot of Web services are available on the Internet and, therefore, it is difficult to find matching Web services among the available Web services. The recommendation systems can help in fixing this problem. In this paper, our observation was based on the recommended method such as the collaborative filtering (CF) technique which faces some failure from the data sparsity and the cold-start problems. To overcome these problems, we first applied an ontology-based clustering and then the k-nearest neighbor (KNN) algorithm for each separate cluster group that effectively increased the data density using the past user interests. Then, user ratings were predicted based on the model-based approach, such as singular value decomposition (SVD) and the predictions used for the recommendation. The evaluation results showed that our proposed approach has a less prediction error rate with high accuracy after analyzing the existing recommendation methods.

• 한국정보과학회논문지:컴퓨팅의 실제 및 레터
• /
• 제16권9호
• /
• pp.927-935
• /
• 2010

웹 2.0 시대에는 웹 상의 사용자들이 수많은 멀티미디어 컨텐츠를 생성함에 따라서 멀티미디어 검색이 더욱 중요하게 되었다. URL, 사진, 동영상과 같은 웹 컨텐츠를 설명하는 간단한 키워드인 태그는, 웹 컨텐츠의 메타데이터 역할을 하고 있다. 태그가 달린 데이터의 양이 많아지면 훨씬 풍부한 메타데이터를 포함한 웹 컨텐츠를 대상으로 검색이 가능하기 때문에 태그를 이용한 검색으로 사용자가 원하는 결과를 찾을 수 있는 가능성이 높아지게 된다. 하지만 실제로 태그를 사용하는 사용자의 수는 많지 않다. 태그를 입력하는 과정이 번거롭기 때문이거나 어떠한 태그를 입력하는 것이 다른 사용자들로부터의 접근성을 높일 수 있는지 모르기 때문이다. 이러한 문제를 해결하기 위해서, 사용자의 태그 입력 과정을 도와주는 기법인 태그 추천이 연구되었다. 사용자가 어떠한 웹 컨텐츠를 게재하려고 할 때, 태그 추천 시스템이 해당 웹 컨텐츠에 적절한 태그를 추천하면, 사용자는 적절한 태그를 선택하는 것으로 태그 입력이 이루어진다. 본 연구에서는 이러한 태깅 시스템에서의 다양한 태그 추천 방법론을 분석하고, 분류하였다.