Algorithms in C
Knowledge discovery in databases: an overview
AI Magazine
Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
A translation approach to portable ontology specifications
Knowledge Acquisition - Special issue: Current issues in knowledge modeling
Agents that reduce work and information overload
Communications of the ACM
GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
Social information filtering: algorithms for automating “word of mouth”
CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Fab: content-based, collaborative recommendation
Communications of the ACM
Advances in knowledge discovery and data mining
Advances in knowledge discovery and data mining
Recommendation as classification: using social and content-based information in recommendation
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
The entity-relationship model—toward a unified view of data
ACM Transactions on Database Systems (TODS) - Special issue: papers from the international conference on very large data bases: September 22–24, 1975, Framingham, MA
Content-based book recommending using learning for text categorization
DL '00 Proceedings of the fifth ACM conference on Digital libraries
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Modern Information Retrieval
E-Commerce Recommendation Applications
Data Mining and Knowledge Discovery
Data Mining: An Overview from a Database Perspective
IEEE Transactions on Knowledge and Data Engineering
Knowledge Discovery in Databases: An Attribute-Oriented Approach
VLDB '92 Proceedings of the 18th International Conference on Very Large Data Bases
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Web Document Classification Based on Fuzzy Association
COMPSAC '02 Proceedings of the 26th International Computer Software and Applications Conference on Prolonging Software Life: Development and Redevelopment
Web Mining: Information and Pattern Discovery on the World Wide Web
ICTAI '97 Proceedings of the 9th International Conference on Tools with Artificial Intelligence
Category cluster discovery from distributed WWW directories
Information Sciences—Informatics and Computer Science: An International Journal - special issue: Knowledge discovery from distributed information sources
A data mining and semantic web framework for building a web-based recommender system
A data mining and semantic web framework for building a web-based recommender system
WIA: a web inspection architecture
International Journal of Knowledge and Web Intelligence
Combining lexical and structural information for static bug localisation
International Journal of Computer Applications in Technology
RESYGEN: A Recommendation System Generator using domain-based heuristics
Expert Systems with Applications: An International Journal
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Traditional search engines require users to form the keyword based query which can accurately depict the search topic. More importantly, search engines are generally unable to customise the results according to the users' preferences. Recently, an alternative approach of retrieving the information, known as the recommender system is proposed. A recommender system is an intermediary program that intelligently generates a list of information which matches the users' preferences. In this paper, a new recommender system framework based on data mining techniques and the Semantic Web concept is proposed. Two information filtering methods for providing the recommended information (i.e., content-based and collaborative filtering) are considered. Both filtering techniques are based on data mining algorithms which provide efficiency in handling large data sets. In addition, the Semantic Web concept, in which the information is given well-defined meaning, is incorporated into the framework in order to provide the users with semantically-enhanced information. To demonstrate the potential use of the proposed framework, a system prototype for recommending the University of Miami's Web pages was implemented to enhance the performance of the traditional query-based information retrieval approach provided on the website.