Using data mining to provide recommendation service

Authors:
Ruey-Shun Chen;Yung-Shun Tsai;K. C. Yeh;D. H. Yu;Yip Bak-Sau
Affiliations:
Department of Information Management, China University of Technology, Hsinchu, Taiwan;Institute of Information Management, National Chiao Tung University, Hsinchu, Taiwan;Institute of Information Management, National Chiao Tung University, Hsinchu, Taiwan;Institute of Information Management, National Chiao Tung University, Hsinchu, Taiwan;Institute of Biotechnology, Department of Life Science, National Tsing-Hua University and Department of Neurology, Hsin-Chu General Hospital, Department of Health, The Executive Yuan, Taiwan
Venue:
WSEAS Transactions on Information Science and Applications
Year:
2008

Citing 13
Cited 7

Using collaborative filtering to weave an information tapestry

Communications of the ACM - Special issue on information filtering
Artificial intelligence: a modern approach

Artificial intelligence: a modern approach
Social information filtering: algorithms for automating “word of mouth”

CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Data mining and knowledge discovery in databases

Communications of the ACM
Fab: content-based, collaborative recommendation

Communications of the ACM
Content-based book recommending using learning for text categorization

DL '00 Proceedings of the fifth ACM conference on Digital libraries
E-Commerce Recommendation Applications

Data Mining and Knowledge Discovery
Mining customer product ratings for personalized marketing

Decision Support Systems - Special issue: Web data mining
Evaluation of web usage mining approaches for user's next request prediction

WIDM '03 Proceedings of the 5th ACM international workshop on Web information and data management
Usage derived recommendations for a video digital library

Journal of Network and Computer Applications
Collaborative recommender systems: Combining effectiveness and efficiency

Expert Systems with Applications: An International Journal
Feature-based recommendations for one-to-one marketing

Expert Systems with Applications: An International Journal
Empirical analysis of predictive algorithms for collaborative filtering

UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence

Knowledge management in the HR sector of RD organizations

WSEAS Transactions on Information Science and Applications
A consumer support architecture for enhancing customer relationships

WSEAS Transactions on Information Science and Applications
Design of an intrusion detection system based on Bayesian networks

WSEAS Transactions on Computers
Relationship between muscle activities and different movement patterns on an unstable platform using data mining

WSEAS Transactions on Information Science and Applications
An ontology-based architecture for consumer support systems

WSEAS Transactions on Information Science and Applications
ICT and disaster preparedness in Malaysia: an exploratory study

WSEAS Transactions on Information Science and Applications
Development the strategics and customer relationship management model for medical center outpatient services

WSEAS Transactions on Information Science and Applications

Quantified Score

Hi-index	0.00

Visualization

Abstract

This research introduces personalized recommendation service into library services. Using the borrowing record of the library as basis, the association rules of data mining technique are used to look for book association by focusing on reader's borrowing mode, personal interest and trait in order to simplify the complexity of recommendation structure. The Bayesian network concept is used to build up a personalized book recommender system in order to generate different book recommendations, ranking from high to low, to help reader to locate book information most suitable to his requirement. Meanwhile we use user satisfaction questionnaire to understand the accuracy of recommended books and further to feedback information in order to help the post learning of Bayesian network parameter. This is for the perfection of the overall structure of recommender system so that readers could make use of the resource of the library more effectively and the value of the library system could be further improved.