Intelligent information-sharing systems
Communications of the ACM
A knowledge-based message management system
ACM Transactions on Information Systems (TOIS)
A general user modelling facility
CHI '88 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
How do experienced information lens users use rules?
CHI '89 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Information retrieval: data structures and algorithms
Information retrieval: data structures and algorithms
Information filtering and information retrieval: two sides of the same coin?
Communications of the ACM - Special issue on information filtering
Modeling users' interests in information filters
Communications of the ACM - Special issue on information filtering
A rule-based message filtering system
ACM Transactions on Information Systems (TOIS)
Stereotypes in information filtering systems
Information Processing and Management: an International Journal
Information Filtering: A New Two-Phase Model Using StereotypicUser Profiling
Journal of Intelligent Information Systems - Special issue: next generation information technologies and systems
Concept features in Re:Agent, an intelligent Email agent
AGENTS '98 Proceedings of the second international conference on Autonomous agents
User profiling in personalization applications through rule discovery and validation
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
ACM Computing Surveys (CSUR)
Decision Support Systems - From information retrieval to knowledge management: enabling technologies and best practices
Rule-based video classification system for basketball video indexing
MULTIMEDIA '00 Proceedings of the 2000 ACM workshops on Multimedia
Information Processing and Management: an International Journal
A Theoretical Study on Six Classifier Fusion Strategies
IEEE Transactions on Pattern Analysis and Machine Intelligence
Introduction to Expert Systems
Introduction to Expert Systems
Information Filtering: Overview of Issues, Research and Systems
User Modeling and User-Adapted Interaction
An empirical testing of user stereotypes of information retrieval systems
Information Processing and Management: an International Journal - Special issue: Cross-language information retrieval
Coauthorship networks and academic literature recommendation
Electronic Commerce Research and Applications
Supporting information spread in a social internetworking scenario
NFMCP'12 Proceedings of the First international conference on New Frontiers in Mining Complex Patterns
| Hi-index | 0.00 |
Rule-based information filtering systems maintain user profiles where the profile consists of a set of filtering rules expressing the user's information filtering policy. Filtering rules may refer to various attributes of the data items subject to the filtering process. In personal rule-based filtering systems, each user has his/her own personal filtering rules. In stereotype rule-based filtering systems, a user is assigned to a group of similar users (his/her stereotype) from which he/she inherits the stereotype's filtering profile. This study compares the effectiveness of the two alternative rule-based filtering methods: stereotype-based rules versus personal rules. We conducted a comparison between filtering effectiveness when using the personal rules or when using the stereotype-based rules. Although, intuitively, personal filtering rules seem to be more effective because each user has his own tailored rules, our comparative study reveals that stereotype filtering rules yield more effective results. We believe that this is because users find it difficult to evaluate their filtering preferences accurately. The results imply that by using a stereotype it is possible not only to overcome the problem of user effort required to generate a manual rule-based profile, but at the same time even provide a better initial user profile.