Fab: content-based, collaborative recommendation
Communications of the ACM
Combining collaborative filtering with personal agents for better recommendations
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
E-Commerce Recommendation Applications
Data Mining and Knowledge Discovery
A Model for XML Schema Integration
EC-WEB '02 Proceedings of the Third International Conference on E-Commerce and Web Technologies
A generalized metric distance between hierarchically partitioned images
MDM '05 Proceedings of the 6th international workshop on Multimedia data mining: mining integrated media and complex data
Efficient bayesian hierarchical user modeling for recommendation system
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Advanced Methods for Inconsistent Knowledge Management (Advanced Information and Knowledge Processing)
Web search personalization with ontological user profiles
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
Ontological User Profiling on Personalized Recommendation in e-Commerce
ICEBE '08 Proceedings of the 2008 IEEE International Conference on e-Business Engineering
A METHOD FOR COMPLEX HIERARCHICAL DATA INTEGRATION
Cybernetics and Systems - KNOWLEDGE PROCESSING METHODOLOGIES IN INTELLIGENT AUTONOMOUS SYSTEMS
User trace-based recommendation system for a digital archive
ICCBR'10 Proceedings of the 18th international conference on Case-Based Reasoning Research and Development
Path-Oriented integration method for complex trees
KES-AMSTA'12 Proceedings of the 6th KES international conference on Agent and Multi-Agent Systems: technologies and applications
Tuning user profiles based on analyzing dynamic preference in document retrieval systems
Multimedia Tools and Applications
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Document recommendation in information retrieval is a well known problem. Recommending a profile in order to personalize document search is a less common approach. In this paper a specific solution to profile recommendation is proposed, by use of knowledge integration methods. A hierarchical user profile is defined to represent the user. For each new user joining an information retrieval system, a prepared non-empty profile is assigned based on other similar users. To create such a profile, knowledge integration methods are used. A set of postulates are proposed to describe such representative profile. Criteria measures are used to determine if a solution to a specific algorithm satisfies these postulates. Three integration algorithms are proposed and evaluated, including a heuristic algorithm. In future research, these algorithms will be used in a practical system.